research journal
2018 / VOL 10.01
www.perkinswill.com
RESEARCH JOURNAL 2018 / VOL 10.01
Editors:
Ajla Aksamija, Ph.D., LEED AP BD+C, CDT and Kalpana Kuttaiah, Associate AIA, LEED AP BD+C
Journal Design & Layout:
Kalpana Kuttaiah, Associate AIA, LEED AP BD+C
Acknowledgements:
We would like to extend our APPRECIATION to everyone who contributed to the research work and articles published within this journal.
Perkins and Will is an interdisciplinary design practice offering services in the areas of Architecture, Interior Design, Branded Environments, Planning + Strategies and Urban Design. Copyright 2018 Perkins and Will All rights reserved.
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TABLE OF CONTENTS JOURNAL OVERVIEW
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EDITORIAL
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01. ENERGY-RELATED OCCUPANT BEHAVIOR SCHEDULE: Rethinking the Occupant Schedule in Energy Simulations Cheney Chen, Ph.D., P.Eng., BEMP, CPHD, LEED AP BD+C..................................................... Page 7 02. INTEGRATION OF PARAMETRIC DESIGN METHODS AND BUILDING PERFORMANCE SIMULATIONS FOR HIGH-PERFORMANCE BUILDINGS: Methods and Tools Ajla Aksamija, PhD, LEED AP BD+C, CDT Dylan Brown ........................................................................................................................ Page 28 03. SPATIAL AND MOTIVATIONAL PROGRAMMING IN OFFICE ENVIRONMENTS: Comparison of Co-Working to Other Types of Commercial Spaces Tina Nguyen, LEED GA ...................................................................................................... Page 54 PEER REVIEWERS ..................................................................... Page 85
AUTHORS
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JOURNAL OVERVIEW The Perkins and Will Research Journal documents research relating to the architectural and design practice. Architectural design requires immense amounts of information for inspiration, creation, and construction of buildings. Considerations for sustainability, innovation, and high-performance designs lead the way of our practice where research is an integral part of the process. The themes included in this journal illustrate types of projects and inquiries undertaken at Perkins and Will and capture research questions, methodologies, and results of these inquiries. The Perkins and Will Research Journal is a peer-reviewed research journal dedicated to documenting and presenting practice-related research associated with buildings and their environments. The unique aspect of this journal is that it conveys practice-oriented research aimed at supporting our teams. This is the nineteenth issue of the Perkins and Will Research Journal. We welcome contributions for future issues.
RESEARCH AT PERKINS AND WILL Research is systematic investigation into existing knowledge in order to discover or revise facts or add to knowledge about a certain topic. In architectural design, we take an existing condition and improve upon it with our design solutions. During the design process we constantly gather and evaluate information from different sources and apply it to solve our design problems, thus creating new information and knowledge. An important part of the research process is documentation and communication. We are sharing combined efforts and findings of Perkins and Will researchers and project teams within this journal. Perkins and Will engages in the following areas of research: • Market-sector related research • Sustainable design • Strategies for operational efficiency • Advanced building technology and performance • Design process benchmarking • Carbon and energy analysis • Organizational behavior
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EDITORIAL This issue of Perkins and Will Research Journal includes three articles that focus on different research topics, such as the potential gaps in energy simulations, methods for integrating parametric design methods with building performance analysis procedures, and exploring well-being, productivity and engagement of employees in different working environments. “Energy-Related Occupant Behavior Schedule: Rethinking the Occupant Schedule in Energy Simulations” highlights on one of the potential gaps in energy simulations, and how static occupancy schedules bring uncertainties to energy models and impact the accuracy of energy modeling outputs. By proposing multiple and flexible approaches to cope with occupancy schedules and energy modeling requirements at different design stages, this article discusses methodologies for more accurate energy modeling. “Integration of Parametric Design Methods and Building Performance Simulations for High-Performance Buildings: Methods and Tools” discusses methods for integrating parametric design with building performance analysis procedures, suitable for whole building design. An ideal framework was developed and tested using existing software applications, including Building Information Modeling (BIM), non-BIM, parametric design and building performance analysis applications. “Spatial and Motivational Programming in Office Environments: Comparison of Co-Working to Other Types of Commercial Spaces” explores different working environments by applying two research methods to measure space use – motivational and spatial programming. Five different case studies were used to explore the commonality and differences of spatial design and goals from different industries. It was determined that all investigated types of commercial spaces benefit from a diverse layout. Ajla Aksamija, PhD, LEED AP BD+C, CDT Kalpana Kuttaiah, Associate AIA, LEED AP BD+C
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Energy-Related Occupant Behavior Schedule
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Energy-Related Occupant Behavior Schedule: Rethinking the Occupant Schedule in Energy Simulations Cheney Chen, Ph.D., P.Eng., BEMP, CPHD, LEED AP BD+C cheney.chen@perkinswill.com
ABSTRACT This research article focuses on one of the potential gaps in energy simulations, static occupancy schedules. Static occupancy schedules bring uncertainties to energy models and impact the accuracy of energy modeling outputs. In order to generate reliable dynamic schedules, a new workflow is proposed that integrates measured occupant behaviors and improved occupancy schedules. This workflow was tested through occupancy measurements, data processing, schedule customization, energy model integration, and eventually energy modeling of a case study building, a commercial office space. The results indicate that measured occupancy schedules result in more accurate energy modeling results. The concluding remarks present multiple and flexible approaches to cope with the occupancy schedules and energy modeling requirements at different design stages. KEYWORDS: occupant schedule, energy models, workflow, static, dynamic
1.0 INTRODUCTION
The increasing environmental concerns regarding worldwide excessive energy use and global warming are the driving forces towards more sustainable use of the diminishing natural resources, especially fossil fuels. As one of the major energy consumers, buildings are associated with 21 percent of the world’s delivered energy consumption, which is necessary to fulfil occupants’ requirements1,2. Therefore, it is critical to understand energy use breakdowns in buildings and promote energy efficient design strategies. Energy modeling is an essential approach to fulfil this purpose3. It has been widely used to compute building energy performances throughout the design process in order to make betterinformed design decisions4. However, energy models may produce inaccurate results (Figure 1). Among a group of surveyed LEED certified buildings, there is a wide gap between energy use predicted by LEED energy models and the actual building energy performance5. There are many possible reasons associated
with the discrepancies6. Other than the limitations embedded in the algorithms of the simulation tools and modeling strategies, energy simulations may rely on some inputs or assumptions that are not well measured or verified in the first place7,8. Occupant behavior is considered as one of the determining variables that impact building energy use, but typically is not well represented in the context of energy simulations. According to Nguyen and Aiello’s survey9, occupancy pattern has shown significant impacts on energy performance of HVAC systems, lighting, appliances, and building controls. Nearly 60 percent energy use difference could be expected between the opposite occupancy scenarios (conservation behavior vs. wasteful one). Since HVAC, lighting, and equipment represent up to 85 percent of the total energy use in buildings10, poorly described occupant behavior in energy simulations certainly brings uncertainties to the energy modeling results11.
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Figure 1: Measured versus proposed savings percentages5.
Due to the significant energy saving potentials, occupancy-related research has drawn considerable attention, addressing three major areas. First, researchers attempted to monitor occupancy behavior for improved understanding of the inter-relationships between its profiles and building energy performances12,13,14,15,16,17. Real-time data was collected as valuable information, since it can be used to optimize applicable occupancy profiles. Following up with the site measurements, occupancy-related energy performance was also widely explored and verified via energy simulations. Predicted results confirmed tangible energy savings, ranging from 10 to 60 percent18,19. The energy savings are actually
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achieved through occupancy-related control (reflected as modified schedules in the simulation programs). Recently, more researchers have focused on occupant behavior simulations20, 21. A research team from Berkeley Labs and Tsinghua University introduced a novel approach for building occupancy simulation. It is based on the Homogeneous Markov chain model to simulate occupants’ stochastic movement process (Figure 2). A web-based occupancy simulator was also developed, which could take high-level input (occupants, spaces and events) for simulating occupant movement and eventually generate occupant schedules for each space defined in the simulator.
Energy-Related Occupant Behavior Schedule
Figure 2: Online occupancy simulator. (Source: http://occupancysimulator.lbl.gov/)
Regardless if they are obtained from measurements or simulations, occupancy schedules deserve to be further studied in order to facilitate more accurate energy calculations from energy modeling perspective. At present, they are not very well defined in a wide variety of energy simulation tools, including but not limited to ESP-r, TRNSYs, DOE-2, BLAST, EnergyPlus, and IES Virtual Environment. It is very common that simplified default occupant schedules (e.g. ASHRAE occupancy schedules) are adopted in these tools for some typical architypes (Figure 3). They may be reasonable, but far from accurate when occupants are assumed to “behave� in a static manner in energy simulations. The potential
dynamic interactions between occupants and building systems are underestimated. By understanding the importance of occupant behavior in buildings and the potential gap embedded in the current simulation tools, the objective of this research was to explore a usable workflow with which energy modelers can ensure more accurate simulation outputs by adopting dynamic occupancy schedules. The hypothesis is that the improved schedule can help close the performance gap caused by default schedules (Figure 4).
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Figure 3: Typical ASHRAE occupancy schedules.
Figure 4: Closing the performance gap through different schedules.
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2.0 METHODOLOGY
A new workflow is proposed in Figure 5, which demonstrates a complete process of addressing occupancy measurements, data processing, schedule customization, energy model integration and eventually energy result verification. This approach relies on dynamic oc-
cupancy schedules in order to account for the complex web of interactions between human behavior and building system performance. It is also anticipated that energy model is able to produce more accurate results by adopting the workflow.
Figure 5: The proposed workflow for improved energy modeling process (occupancy measurements - data processing - schedule customization - energy model integration - energy result verification).
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2.1 Occupancy Measurements
Occupancy measurement is apparently a straightforward way to record human behavior in buildings. However, it might be time-consuming to capture any irregular human behavior that requires a long period of monitoring. A field measurement was carried out in the Perkins and Will Vancouver office from 19 June to 12 September 2017. Due to the time constraint, five archetypal locations (four space occupancy types) were chosen, and each location was measured for a period of two weeks (Figure 6). As a simple office environment,
Figure 6: The locations of the occupancy measurements.
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it was expected that the occupancy behaviors in the office environment are regular and trackable. The twoweek period would be considered enough to capture the regular patterns of human behavior in these spaces. The four typical office occupancy types covered in the measurements were: • Meeting room (Basement boardroom, 3rd floor meeting room) • Office workspace (3rd floor staff workstation) • Kitchen (3rd floor kitchen/copy) • Lavatory (3rd floor washroom).
Energy-Related Occupant Behavior Schedule
In the measurements, two types of data loggers (Figure 7) were used to measure occupants’ behavior and some environmental variables: • HOBO Occupancy & Light data logger monitors room occupancy and indoor light changes to identify occupancy patterns and determine energy usage and potential savings. • HOBO Temperature/Relative Humidity (RH) data logger records temperature and relative
humidity (within 3.5 percent accuracy) in indoor environments with its integrated sensors. Occupancy behavior was the key parameter in this research, but lighting (on/off), ambient air temperature, and RH were also measured as valuable references to help analyze occupancy patterns in the rooms.
Figure 7: Data loggers.
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2.2 Data Processing
When all the measurements were completed, the raw data (downloaded directly from the data loggers) did not indicate structured information. In order to understand the data, some processing was necessary to con-
Figure 8: Converting raw data to derived information.
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vert raw data into useful information. A typical 24-hr schedule at 10-min interval was generated based on the two-week data collection (Figure 8). Data obtained from the weekdays and the weekends were treated separately for each space.
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2.3 Schedule Customization
The online occupancy simulator takes “high level” input for occupants, spaces and events before any usable occupant schedule for a given space could be generated. Schedule customization (Figure 9) was performed to ensure the measured occupancy behavior, including
arrival/departure/short term leaving and other possible “events”, can be reflected as high quality inputs adopted by the online occupancy simulator. Meanwhile, time allocated to the various events could be identified and used for the occupancy simulator with confidence.
Figure 9: Calibrating occupancy simulation by measured typical schedule.
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2.4 Energy Model Integration
Although the simulated schedules can be downloaded as .csv or .idf format and used for EnergyPlus simulations, they are not directly usable for other energy simulation tools, such as IES Virtual Environment. Therefore, the next step required conversion of the simulated schedules into an IES Virtual Environment compatible. Usually occupancy schedules could be generated in simulation tools quickly with limited data inputs (e.g. 24 inputs represent a typical day in a schedule). However,
Figure 10: Manual input versus ERGON.
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manual schedule input becomes an unrealistic approach when hundreds or even thousands of data entry points are necessary for a full year dynamic schedule, especially with a very narrow time-step. Therefore, IESERGON (Figure 10) was chosen as a cloud service to import .csv files, graphically interrogate the completeness of data, undertake some initial analysis using inbuild analytics, and export as a Free Form Profile Data file (.ffd) into IES Virtual Environment simulations.
Energy-Related Occupant Behavior Schedule
2.5 Energy Result Verification
Finally, a case study was modeled by using IES Virtual Environment in order to test the workflow and its deliverable, dynamic occupancy schedules. Perkins and Will Vancouver office was modeled twice using rapid energy modeling strategy22. This strategy relies on three simple steps: capture, model and analyze, and is appli-
cable to capturing existing building conditions. Without changing any other modeling parameters or assumptions adopted by the rapid energy modeling strategy, default occupancy schedules and measured/simulated ones were adopted in before- and an after- energy simulations. The calculated energy results were compared to an energy audit report.
Figure 11: Rapid energy modeling of Perkins and Will Vancouver office by using IES-VE.
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3.0 RESULTS AND DISCUSSION
the individual staff behavior). Unfortunately, due to the time constraint, the workstation measurement focused on a single staff only. It would be more representative if the occupant sampling size (i.e. various staff in the office including design staff, project managers, marketing staff) could be increased.
3.1 Field Measurements and Schedules
It should be noted that the measured staff schedule aligns with ASHREA office occupancy schedule perfectly (Figure 13). On one hand, it indicates that the ASHRAE default schedule is not an arbitrary assumption, but reasonably based on previous measurements or observations. But, ASHRAE default schedules are limited (nine types) and static in terms of defining the dynamic movement of occupants within buildings. In most cases, occupant numbers are overestimated since the diversity of building occupancy cannot be captured when default ASHRAE schedules are applied to each space.
Perkins and Will Vancouver office’s specific occupancy behaviors and energy performance are reported through the results obtained from both the measurements and the energy simulations.
In an office environment, staff behavior is a critical piece in shaping the office’s occupancy pattern. A typical 24 hr staff schedule is presented in Figure 12. On average, 80 to 90 percent of workstation occupancy during working hours was observed, whereas 10 to 20 percent working hours are dedicated for some off-workstation activities, such as internal or external meetings, site visits, washroom/kitchen visits, etc. During lunch time, staff desk occupancy is dropped down to 50 percent. Outside of working hours, 5 to 10 percent overtime was observed until 9 pm (this is highly relevant to
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Figure 12: Measured schedule – staff.
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Figure 13: ASHRAE schedule – staff.
The measurements conducted in the two meeting rooms generated schedules that are beyond the scope of typical ASHRAE schedules. They indicate unique meeting occupancy behaviors (Figure 14). The boardroom in the basement is extremely popular during morning from 10 am to 12 pm and up to 80 percent occupancy around noon is observed. The reason could be that many formal management meetings and client meetings are held in this space during this specific time. After the peak hours, the occupancy decreases to around 30 to 40 percent. Usually project teams would take over the space and use it as a project meeting place similar to other meeting rooms in the building. There is still an averaged 10 to 15 percent occupancy from 6 to 9 pm when extended meetings or informal discussions are held occasionally in the basement. Compared to the large boardroom in the basement, the 3rd floor meeting room represents typical project meeting places, which occur at each floor. The occupancy pattern is featured with evenly distributed occupancy in the morning and the afternoon. The peak time is in early morning and late afternoon with around 60 percent oc-
cupancy. Aligning with the staff behavior, the meeting room occupancy is reduced down to 20 percent during lunch hours. Extended meetings are also held in the room after the working hours with less than 10 percent occupancy. Meeting room schedule is a good example to explain complexities of occupancy pattern within the building. Besides some transient occupants (visitors), the meeting rooms are actually occupied by the building’s staff who move from their workstations to the meeting places. The occupancy patterns (both workstation and meeting room) determine the location of the occupants in each time period within the building. Most importantly, when they are located in a space (either workstation or meeting space), they will trigger the operation of the building systems, such as lights, HVAC and windows, and impact the energy use of the occupied space as well as the space they left. Without considering the diversity of occupants but simply applying static schedules into each space would ignore such dynamic operation and overestimate energy use.
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Measured boardrm boardrm weekdays (a) Measured weekdays 100% 90% 80% 70% 60% 50% 40% 30% 20% 10%
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Figure 14: Measured schedule – Conference room: (a) Board room; (b) 3rd floor meeting.
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Energy-Related Occupant Behavior Schedule
Kitchen/copy room is not covered by ASHRAE default schedules although it is apparently a popular space visited by the building occupants at each floor. From the measurements, the kitchen is frequently visited from 8 am until 8 pm throughout a day. Afterwards, janitorial contractors and overtime staff’s activities are still observed until midnight. It is expected that the kitchen occupancy is not high in each time period compared as workstations or meeting rooms. The reason is that the activities happening in the space are usually shortterm, although it serves about 30 occupants on each floor. It is interesting to observe the two peaks (around 50 percent occupancy) at around 8 to 10 am and 12
to 2 pm that echo breakfast- and lunch-related activities. Similar to the workstation and the meeting place, occupants within the kitchen will trigger the operation of kitchen/copy systems, such as water fixtures, micro oven, coffee machine and printers. In order to understand kitchen energy use pattern better, the activities reflected in the schedule are translated to an effective use time per day. An average of 220 minutes of kitchen/ copy room use per day is calculated. Without sub-metering installed in the place, the effective use time per day could be useful for estimating the energy use of kitchen/copy systems.
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Figure 15: Measured schedule – Kitchen.
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On average, 20 to 30 percent occupancy throughout a day can be observed in the measured lavatory. Considering the ratio of users per lavatory per floor at 15/1 (the assumption is 30 staff per floor with 15 males and 15 females), the consistent occupancy is acceptable and explains staff’s behavior other than in the workstations, the meeting rooms, the kitchens and outside of the building. Similar to the kitchen, the activities reflected
in the schedule can be translated into a total of 121 effective minutes of lavatory use per day or 8 minutes per person per day. Leadership in Energy and Environmental Design (LEED) assumes default water closet use rate as 3 times a day for building occupants. The observations obtain from the lavatory align with the LEED assumptions and they are useful for estimating water use and lighting energy use in the lavatory.
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Figure 16: Measured schedule – Lavatory.
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3.2 Before and After Energy Simulation
A before and after energy simulation was performed. By following the rapid energy modeling strategy, Perkins and Will Vancouver office was first modeled with respect to the following aspects: • ASHRAE default office building schedules (occupant, lighting and equipment) • Office drawings/layout/dimensions • Building envelope thermal performance estimations by walkthrough • Building HVAC system walkthrough • Building lighting system walkthrough • Building equipment specification sheets • Weather file - Vancouver 718920 (CWEC). By comparing the rapid energy modelling results and a Level 2 Energy Audit for LEED O&M EAc2.1 report (Figure 17), it is observed that the total energy difference is at 16 percent, which fulfils the initial expectation of the
variance within 20 percent. However, the larger differences, up to 146 percent, are observed when energy breakdowns are compared in detail. Hot water, artificial lighting and equipment energy use are all closely related to the occupancy behaviors in the building. Apparently, the ASHRAE default occupancy schedules do not track the interrelationship between occupant and building system energy use correctly, and trigger the discrepancies. On the other hand, HVAC related enduses, such as fan energy, heating and cooling energy, are also off-track. However, HVAC energy use is much more complicated because these systems are impacted by not only occupancy behavior but also micro-climate, building envelope performance, internal heat gain, infiltration, natural ventilation strategy, and system performance. Therefore, HVAC related energy use may not have strong correlation with occupancy behavior and their accuracy is beyond the current research scope.
Figure 17: A comparison of REM and Level 2 Energy Audit results (with ASHRAE schedules).
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The energy simulation was conducted again by only adopting the dynamic full-year schedules, calibrated and produced by the measurements and the online occupant simulator. For the three occupancy driven energy uses (hot water, artificial lighting and equipment/plug loads), their accuracy has been greatly improved (Figure 18). The improvement is observed simply because the new schedules can capture the occupant driven energy uses more accurately. However, HVAC related energy
is still far from the baseline and it indicates the energy simulation accuracy is not only affected by occupancy schedules alone. Interestingly, both space heating energy and cooling energy are also improved slightly in after simulation by roughly 10 to 20 percent (-59 percent vs. -34 percent & 76 percent vs. 66 percent). The contribution is from the corrected internal gains, including occupant, lighting and equipment, which are all improved by the dynamic schedules.
Figure 18: A comparison of REM with calibrated schedules and Level 2 Energy Audit results (with measured schedules).
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Energy-Related Occupant Behavior Schedule
3.3 Further Considerations
The results indicate that the proposed workflow is able to improve the accuracy of energy simulations through introducing detailed occupancy schedules. On the other hand, there are more ways to produce usable occupancy schedules for energy modeling use. The potential schedule generation workflows could be simply summarized as Default Schedules (DS), Simulated Schedules (SS), Measured & Simulated Schedules (MSS) and Measured Schedules (MS) (Figure 19). DS is widely used in the energy modeling world but MS, MSS, and SS are all relatively new ones. By taking different schedule generation strategies, different levels of accuracy could be anticipated. Although MS, MSS or even SS could generate more accurate energy results, the penalty is always associated with the time-consuming process, especially for MS and MSS.
Instead of using one single approach throughout all the design phrases, energy modelers should consider how to use these workflows in a smart way by addressing their pros and cons (Figure 20). DS could still be useful at initial design stage when better assumptions or detailed information are not available from design team or client. Meanwhile, the accuracy of early stage energy modeling is not critical, since sensitivity or parametric studies could still be able to drive the design team to the right design decisions. MSS and MS could update energy model by improving its accuracy when more detailed information is to be measured or simulated at CD or DD stages. It alights with the expectation of more accurate energy prediction at these design stages. SS is dedicated to measurement and verification modeling when building metering systems are well established and all the detailed information could be measured precisely over a long period of time.
Figure 19: Schedule generation approaches.
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Figure 20: Pros and Cons of different schedule generation strategies.
4.0 CONCLUSIONS
This research focuses on one of the potential gaps, occupancy schedule, which brings uncertainties to energy models and impacts the accuracy of energy modeling results. A new workflow was therefore proposed and tested through the occupancy measurements, data processing, schedule customization, and energy model integration. A specific case study was used to test the workflow, and this article discusses results in detail. The current research has the following limitations, which may require further studies: • Due to the time constraint, only one staff workstation was measured. The behaviors will be slightly different for all employees in the building. Although it would be difficult to monitor each staff member, at least representative occupant groups, such as design staff, marketing staff, managing staff, should be measured individually in the future. • The research focuses on the MSS workflow. A quantitative cross-comparison of all the four approaches, MS, MSS, SS and DS, would be helpful to guide when and how each workflow could be employed in energy modeling for appropriate design assistance.
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• Not only human behavior, but all the measurable variables, such as water flow, daylighting and lighting, microclimate, etc. could be integrated into the similar workflow for more accurate energy simulations. This approach would work well with the new LEED v4 metering requirements, which encourages extensive data collection in buildings.
Acknowledgements
The author would like to express his gratitude to Perkins and Will for the generous research funding with which his 2017 Innovation Incubator project has been completed successfully. The gratitude also extends the management team of Perkins and Will Vancouver office for their continuous support. Final acknowledgement is due to Kathy Wardle for her kind help in refining this paper.
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[14] Allcott, H., (2010). “Behavior and Energy Policy”, Science, Vol. 327, pp. 1204–1205.
[5] New Buildings Institute, (2008). Energy Performance of LEED for New Construction Buildings, Report, Retrieved on 3/2018 from https://newbuildings.org/ resource/energy-performance-leed-new-constructionbuildings/. [6] Holladay, M., (2012). Musings of and Energy Nerd: Contemplating Residential Energy Use, Retrieved on 3/2018 from http://www.greenbuildingadvisor.com/ blogs/dept/musings/energy-modeling-isn-t-very-accurate. [7] Meneze, A., Cripps, A., Bouchlaghem, D., and Buswell, R., (2012). “Predicted vs. Actual Energy Performance of Non-Domestic Buildings: Using Post-Occupancy Evaluation Data to Reduce the Performance Gap”, Applied Energy, Vol. 97, pp. 355–364. [8] Bordass, B., Cohen, R., Standeven, M., and Leaman, A., (2001). “Assessing Building Performance in Use 3: Energy Performance of Probe Buildings”, Building Research and Information, Vol. 29, pp. 114–28. [9] Nguyen, T., and Aiello, M. (2013). “Energy Intelligent Buildings based on User Activity: A Survey”, Energy and Buildings, Vol. 56, pp. 244-257. [10] National Resource Canada, (2011). 15th Edition of Energy Efficiency Trends in Canada, Report, Retrieved on 3/2018 from www.nrcan.gc.ca. [11] Clevenger, C., and Haymaker, J., (2006). “The Impact of the Building Occupant on Energy Modeling Simulations”, Conference Proceedings - Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada. [12] Ueno, T., Sano, F., Saeki, O., and Tsuji, K., (2006). “Effectiveness of an Energy-Consumption Information
[15] Faruqui, A., Sergici, S., and Sharif, A., (2010). “The Impact of Informational Feedback on Energy Consumption—A Survey of the Experimental Evidence”, Energy, Vol. 35, No. 4, pp. 1598-1608. [16] Peschiera, G., and Taylor, J., (2012). “The Impact of Peer Network Position on Electricity Consumption in Building Occupant Networks Utilizing Energy Feedback Systems,” Energy and Buildings, Vol. 49, pp. 584-590. [17] Vassileva, I., Odlare, M., Wallin, F., and Dahlquist, E., (2012). “The Impact of Consumers’ Feedback Preferences on Domestic Electricity Consumption”, Applied Energy, Vol. 39, pp. 575–582. [18] Li, N., and Becerik-Gerber, B., (2011). “Performance-Based Evaluation of RFID-Based Indoor Location Sensing Solutions for the Built Environment”, Advanced Engineering Informatics, Vol. 25, No. 3, pp. 535–546. [19] Masoudifar, Na., Hammad, A., and Rezaee, M., (2014). “Monitoring Occupancy and Office Equipment Energy Consumption Using Real-Time Location System and Wireless Energy Meters”, Proceedings of the Winter Simulation Conference, pp. 1108-1119 [20] Feng, X., Yan, D., and Hong, T., (2015). “Simulation of Occupancy in Buildings”, Energy and Buildings, Vol. 87, pp. 348-359. [21] Wang, C., Yan, D., and Jiang, Y., (2011). “A Novel Approach for Building Occupancy Simulation”, Building Simulation, Vol. 4, No. 2, pp. 149-167. [22] Autodesk, (2011). Streamlining Energy Analysis of Existing Buildings with Rapid Energy Modeling, Retrieved on 3/2018 from http://images.autodesk.com/ adsk/files/rem_white_paper_2011.pdf.
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02.
INTEGRATION OF PARAMETRIC DESIGN METHODS AND BUILDING PERFORMANCE SIMULATIONS FOR HIGH-PERFORMANCE BUILDINGS: Methods and Tools Ajla Aksamija, PhD, LEED AP BD+C, CDT, ajla.aksamija@perkinswill.com Dylan Brown, dtdyalnb@gmail.com ABSTRACT This article discusses methods for integrating parametric design methods with building performance analysis procedures, suitable for whole building design. In this research, an ideal framework was developed and tested using existing software applications, including Building Information Modeling (BIM), non-BIM, parametric design and building performance analysis applications. Current software programs that can integrate some form of building performance simulation with parametric modelling include Rhino 3D (non-BIM), Revit (BIM), and SketchUp (non-BIM). Revit and Rhino have visual programming plugins to aid in the creation of parametric forms. In this research, three different workflows were tested, which integrate building performance analysis applications. Specifically, Honeybee and Ladybug (for Rhino 3D), Insight 360 (for Revit) and Sefaira (for Revit) were evaluated. A case study building was used to test and evaluate the workflows, interoperability, modeling strategies and results. Three different performance aspects were analyzed: 1) energy analysis, 2) solar radiation, and 3) daylighting. Simulation results were recorded and analyzed. However, besides simulation results, this article provides an in-depth discussion of the modeling procedures, parametric capabilities, ease of integration and interoperability between different software applications. The results show a promising course for integrating parametric design with building performance simulations. Each evaluated workflow has certain benefits and drawbacks, which are discussed in the article. KEYWORDS: parametric design, high-performance buildings, simulations, building performance analysis, building information modeling
1.0 INTRODUCTION
Advances in building performance simulations have enabled designers to better understand how environmental factors affect building performance, since the impact of various design decisions can be simulated and quantified during the design process1. Parametric design methods, on the other hand, allow designers to generate and explore geometries of building elements by manipulating certain parameters. There are a number of software platforms that focus individually
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on environmental analysis or parametric design, but few integrate both. Common parametric design tools include Grasshopper (Rhino 3D), Dynamo (Autodesk Revit) and GenerativeComponents (Bentley MicroStation). Environmental and energy analysis tools include Insight 360 and Green Building Studio (Autodesk Revit), Sefaira (Autodesk Revit + SketchUp), Radiance, OpenStudio, EnergyPlus, eQuest, DesignBuilder, IES VE, and many others.
Integration of Parametric Design Methods and Building Performance Simulations
The most common method of integrating building performance simulations (BPS) into early design work requires exporting the design model (whole building, partial building, or model of a building component) to a dedicated analysis tool to generate an analysis model, assign input parameters necessary for calculations and simulate energy usage, daylighting, or solar radiation. The results from these simulations are typically used to manually adjust the design model in the original design program, and then the model is exported again, thus repeating the process. This results in an inefficient workflow, as the modelling environment is not integrated with the simulation environment. Further, if design iterations occur, the export/simulation process requires runs for each variant, greatly increasing the time required to complete the analysis. By integrating the capabilities of parametric design and BPS, multiple design variables could be tested rapidly, creating a more cohesive design process. The next section reviews existing literature pertaining to integration of parametric design and performance analysis procedures.
2.0 LITERATURE REVIEW
Typical energy modeling programs are often complex for architects to use during the early stages of design,
resulting in building performance analysis being performed at later stages of design process2, 3. This type of modeling is typically conducted by specialists, who trade information back and forth with the designers, as seen in Figure 1. As a result, the architectural profession lacks established methodologies and protocols that incorporate performative analyses into the early stages of design4. The crucial design decisions that have the most significant impacts on building performance are made at the conceptual stage of a project, such as building massing, orientation, volume, shading, daylight strategies, etc. Collaboration between different disciplines within the design team in this stage becomes very important5. Tools that shift the building performance assessment into conceptual stages of design will improve design process, and will have a bigger effect on building performance6. This introduces the concept of performancebased design, where the energy performance becomes the guiding factor in design. However, majority of current design software applications are not capable of integrating the results from performance-based simulations back into the design model. It is the designer who interprets the results and optimizes the model based on simulation results7.
Figure 1: Traditional approach to energy modeling, relying on information exchange between architects and engineers.
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Parametric modeling relies on geometric representation of a design with components and attributes that can be parametrically varied, where each geometric configuration that derives from parametric variations is called an instance. Instances represent a unique set of transformations based on parametric inputs, generating design variations and different configurations8. Parametric modeling has the potential to overcome current design process limitations and to facilitate the revelation and comparison of performative solutions. Parametric modeling initially lacked applications in architectural design; however, new architectural tools have been developed in the last decade. The ability to produce many instances that result in unique configurations of the same geometric component is the main advantage of parametric modeling. Theoretically, there are three different ways that BPS and parametric modeling can be integrated, as shown in Figure 2. The central model is based on BIM, a widely used central framework concept. Tools read and write to the same model, and allow interchange of semantic information from a design tool to a BPS environment. Because design and BPS tools have recently started exchanging data, it is unclear if the design software or the BPS software should handle convergence. The geometric model and the calculation model live in a shared
format, allowing every tool to operate in the model. A lack of software support and user support for open file formats complicates the central model method, even though the open file formats integrate seamlessly9. In the combined model approach, the modelling and simulation aspects are run in the same environment. The advantage of a combined model is that the design tool functionalities and the simulation tool functionalities are essentially integrated, enabling tool domain integration. The main disadvantage of this method is that the user is restricted to the options and features offered by an environment or program9. Distributed model method is a response to the limitations of the centralized model concept. Distributed models are characterized by deep integration at the model level through the utilization of a middleware component to translate data between the design and BPS tools. Middleware is a system that can filter, modify and extend operator definitions so that definitions reflect the needs of BPS environments, and should not be considered only a converter between formats and platforms. Middleware is an essential part of a distributed model. Its flexibility, features, and usability are essential for model interoperability convergence. Integrated dynamic models are distributed models, where middleware consists of a visual programming language (VPL).
Figure 2: Models for integrated parametric design and building performance simulations.
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Integration of Parametric Design Methods and Building Performance Simulations
Design tools and BPS are largely independent platforms, making integration at runtime level difficult to achieve. The combined model required a certain software application to be both a design and a BPS tool, but currently these types of software applications do not exist. The central model method requires a design tool to be coupled to a BPS tool. It operates within a standardized file format or schema, such as IFC, while the distributed model method couples a design tool through middleware to a BPS tool. An integrated dynamic model is a variation on the distributed model, where middleware consists of a VPL. Each method provides different benefits and poses different limitations. In this research, the main goal was to investigate existing software applications and methods that provide some level of integration, and to document procedures, benefits and limitations.
3.0 RESEARCH OBJECTIVES AND METHODS
The focus of this research was to investigate how parametric design and building simulations can be integrated. These following research objectives were addressed: • Investigation of methods for integrating performance-based design with parametric modelling, focusing on whole building design.
• Investigation of tools and software programs that can seamlessly integrate performancebased design with parametric modelling, particularly focusing on energy analysis, solar radiation analysis and daylight simulations. • Testing the procedures on a specific case study and documenting the results. Current software applications that integrate some form of BPS with parametric modelling include Rhino 3D, Revit, and SketchUp, as shown in Figure 3. Revit is a BIM-based design tool, while Rhino and SketchUp are non-BIM based. Revit and Rhino each have visual programming plugins to aid in the creation of parametric forms. And, a variety of BPS tools are available that address different aspects of performance analysis, including solar radiation analysis, energy modeling and daylight analysis, as shown in Figure 3. This research investigated three different workflows, including integration of both BIM and non-BIM design platforms with parametric modeling and BPS. Specifically, Rhino 3D (with Grasshopper, Honeybee and Ladybug plugins) and Revit with Insight 360 and Sefaira workflows were investigated. Figure 3 shows details of all three investigated workflows, and relationships among software applications. The next section reviews properties of the investigated programs.
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Figure 3: BPS and parametric workflows for Rhino, Revit and SketchUp. The workflows and software applications explored in this research are shown in red.
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Integration of Parametric Design Methods and Building Performance Simulations
4.0 OVERVIEW OF PERFORMANCE-BASED PARAMETRIC DESIGN METHODS AND TOOLS 4.1 Framework for Integration of PerformanceBased and Parametric Design Methods
The ideal framework for integrating parametric and performance-based design is shown in Figure 4. Parametric modelling, geometric preparation and analysis
preparation should be streamlined and connected to performance analysis. This would combine parametric control of building geometry, building systems, and simulation inputs with analysis and visualization of results. This framework was specifically tested for energy simulations, solar radiation and daylight simulations. The investigated design, parametric and performance tools are discussed in the next section.
Figure 4: Ideal framework for parametric and performance-based design.
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4.2 Rhino Workflow
Rhino is a 3D computer-aided design program, which is based on the NURBS mathematical model, as opposed to a polygon-based model. Parametric capabilities in Rhino are achieved through Grasshopper, a publicly available parametric form generating plugin. It is a visual programming tool that allows geometry to be manipulated by a set of constraints defined by the designer. By adjusting the constraints, changes in the model can be seen in real time. The ability of the plugin ranges from simple tasks, such as dividing a line into points, or highly complex tasks, such as generating the facade of a building from a multitude of environmental inputs and user decisions. Ladybug and Honeybee are two open source plugins for Grasshopper and Rhino that help explore and evaluate environmental performance. Ladybug imports standard EnergyPlus weather files (.epw) into Grasshopper and provides a variety of 3D interactive graphics to support the decision-making process during the initial stages of design. Honeybee connects the visual programming environment of Grasshopper to validated simulation engines (EnergyPlus, Radiance and Daysim), which evaluate building energy consumption, comfort, and daylighting10. These plugins enable a dynamic coupling between the flexible, component-based, visual programming interface of Grasshopper, and validated environmental data sets and simulation engines. Ladybug and Honeybee are accessed from Grasshopper, and have their own functions within software interface. Components are dragged onto the Grasshopper canvas, where connectors can be drawn between them to establish different relationships, generate geometry, perform a function, or run an analysis. Sefaira utilizes the same simulation engines as Honeybee.
4.3 Revit Workflow
Revit is a BIM software, commonly used in architectural profession. Insight 360 is a cloud-based analysis software that integrates within the Revit workflow. In 2014, this tool was launched as EnergyPlus Cloud, and then in 2015, it was unveiled as Insight 36011. It is capable
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of performing whole-building energy analysis using EnergyPlus, solar radiation analysis and lighting analysis. Analysis results can be compared to ASHRAE 90.1 and Architecture 2030 benchmarks. Sefaira is a plugin for Revit and SketchUp, which provides building-performance simulations relating to daylight, solar radiation and energy use. Sefaira started as a web-based design tool in 2009, with release of SketchUp plugin in 2013, and Revit plugin in 2014. Analysis results are displayed in real-time on a performance dashboard, and the cloud-based calculation is used to run models through different simulation engines to provide faster analysis.
5.0 ANALYSIS, SIMULATION TYPES AND PARAMETRIC OPTIONS 5.1 Rhino, Ladybug and Honeybee
Multiple types of simulations can be performed with Ladybug and Honeybee, and the most common are discussed below. Ladybug can perform sun path and shadow range analysis, solar radiation analysis, outdoor comfort and create psychrometric charts, as well as wind roses. Honeybee can perform energy, daylight, and natural ventilation analysis, as well as heat transfer analysis. Simulations can be customized to refine the results using parametric methods. For example, the analysis period component specifies the time period for the simulation, and the legend parameter component can be used to color and label the simulation results. Honeybee has several components that allow control of the geometry within the model, without modelling the specific assemblies. This enables the designer to use the input parameters to modify or generate forms more quickly than modelling by hand. The components that were used in this research are grouped into three categories, depending on their function, shown in Table 1. The names of the components are listed to the left, while typical parameters are on the right.
Integration of Parametric Design Methods and Building Performance Simulations
Table 1: Parametrically controlled options for Ladybug and Honeybee. Component Name
Component Parameters
Geometry Modifying
Ladybug Shading Designer Honeybee Split Building Mass Honeybee Glazing Based on Ratio Honeybee EnergyPlus Window Shade Generator
Shading depth, angle, orientation, and number of shading devices Floor-to-floor height Perimeter zone depth
Material Modifying
Honeybee EnergyPlus Opaque Material Honeybee EnergyPlus Window Material Honeybee Radiance Opaque Material Honeybee Radiance Glass Material
U-value and R-value SHGC and Vt RGB reflectance Roughness and specularity Refractive Index
Systems Modifying
Honeybee Set EnergyPlus Zone Loads Honeybee Set EnergyPlus Zone Schedules Honeybee Set EnergyPlus Zone Thresholds Honeybee OpenStudio Systems
Equipment and lighting loads and schedules Ventilation loads and schedules Occupancy schedules Heating and cooling schedules Heating and cooling set-points HVAC system parameters
Simulation Modifying
Ladybug Analysis Period Ladybug Legend Parameters Ladybug Select Sky MTX Honeybee Generate EP Output Honeybee RAD Parameters Honeybee Analysis Recipe
To & from: Month/Day/Hour EnergyPlus simulation outputs Low & high bound Legend scale, colors, etc. Sky selection based on weather file Radiance rendering parameters Analysis grid location and size
Figure 5 shows the general operation of a specific Grasshopper component. In this example, the U-value, Solar Heat Gain Coefficient (SHGC), and Visible Transmittance (Vt) are specified for facade glazing within the Honeybee EnergyPlus Window Material component. This EnergyPlus material is then assigned a name, and assigned to every window in the building. In the geometry preparation stage, the building geometry is brought into Honeybee as zones, depending on the building form and program. Loads and schedules are subsequently assigned to each zone. The thermal and visual properties of building elements, such as walls, floors and windows can be altered for the purposes of energy or daylight simulations. Additionally,
glazing and shading properties are assigned, while mechanical system selection and parameters are set. In the analysis preparation stage, the type of analysis is selected, and parameters that control the simulation are assigned. All simulations can use the analysis period and weather file as inputs, while other inputs are simulation specific. The Ladybug Radiation Analysis can utilize context (surroundings buildings) and sky matrix (hourly radiation data generated by Radiance), while the Honeybee Run Daylight Simulation inputs the Analysis Recipe, which controls the sky type, analysis grid size and height, and Radiance rendering parameters. In the analysis simulation stage, the prepared geometry is run through their respective simulation engines. En-
Figure 5: Component arrangement for creating a new EnergyPlus glass material in Grasshopper.
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ergy simulations use EnergyPlus, while daylight simulations use Radiance and Daysim. Radiation simulations use Ladybug’s native radiation analysis component. Finally, in the visualize results stage, the data from the simulations is displayed according to the analysis type. Data from energy simulations is typically displayed as Energy Usage Intensity (EUI). Data for daylight and radiation simulations is typically shown as a colored grid with values on each grid point.
5.2 Revit and Insight 360
Insight 360 and Sefaira are not capable of parametrically altering the model geometry, at least not before they are uploaded to the cloud. This mainly has to be completed in Revit before simulations can be run on the model. Revit is a parametric software tool by nature, but the parametric modeling tools for quick building massing and building options are limited, compared to Honeybee. Insight 360 is used to perform energy, lighting, and solar radiation analysis.
5.2.1 Energy Analysis Energy analysis requires an energy model to be created before simulations can be run. The energy model can use geometry from conceptual masses or building elements. In the building elements mode, detailed components, such as floors, walls, roofs, curtain walls, windows, etc., are used. Revit does not include easy-touse options for parametrically adjusting detailed building elements, such as window-to-wall ratio, or shading depth, without relying upon the use of dimensions, family parameters, or complex calculations. Alternatively, conceptual masses can be used for the energy model. Using conceptual masses, a mass is drawn in the Revit modeling environment, and the parameters listed in Table 2 are configurable. Glazing percentage and shading options can only be configured for all building orientations. System modifying parameters also apply to the whole building, and cannot be configured on a per-zone or floor basis. Mate-
Table 2: Parametric options for conceptual masses in Revit. Component Name
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Geometry Modifying
Perimeter zone depth and perimeter zone division Glazing percentage (all orientations) Sill height Mass from levels (all masses) Ground plane (level) Glazing shading and shade depth (all orientations) Skylight glazing percentage, width and depth
Conceptual Types (Material Modifying)
Exterior, interior & underground wall Roof, floor & slab Glazing, skylight, shade & opening.
Systems Modifying
Building type, operating schedule, and HVAC system (whole building) Outdoor air requirements (CFM/person, CFM/SF, or ACH)
Simulation Modifying
Project phase (new or existing) Analytical space resolution Analytical surface resolution Mode (use building elements, conceptual elements, or both)
Integration of Parametric Design Methods and Building Performance Simulations
rial modifying parameters behave the same way, where the construction properties affect all of the model element types in the building. The energy model cannot be used for lighting or solar analysis, although the masses used for the conceptual energy model can be used for the solar radiation analysis. In the geometry preparation stage, the type of material selections in the model depends upon the analysis type. For energy analysis, the thermal conductance can either be determined by the conceptual or schematic types in the energy model settings, or the structure of detailed construction elements. The thermal properties for other elements, such as windows and doors, are assigned in the family type parameters. Lighting analysis uses the reflectivity of detailed elements and visible transmittance (Vt) of glass. The reflectivity is assigned by the color under the graphics section in the appearance tab of the material editor. An RGB value of 0,0,0 (black) is 0% reflective while a value of 255, 255, 255 (white) is 100% reflective. Setting the Vt follows a similar process, where the glass used in the window has to be assigned a custom color that corresponds to the correct value. Solar radiation analysis is only concerned with the form of the geometry, so material properties are not used. The analysis preparation stage is controlled by the analysis options that are configurable before running the simulations. The preparation options are straightforward to use, although some of the options are limiting. After the options have been configured, the simulation is run, either locally, or on the cloud. Results can be visualized differently, depending on the type of analysis and simulation type. Energy analysis results are viewed within the Insight 360 dashboard, which can be viewed inside of Revit, or in a web browser. Lighting results are saved into the model, and displayed in a separate floor plan or 3D view. If a new simulation is run, those results overwrite the existing results in the floorplan, but previous simulations can be reimported to a new floor plan. Solar radiation results are displayed in the same view in which the analysis was run. 5.2.2 Daylight Analysis Daylight analysis is run on the cloud, and has been validated against Radiance. Simulations can only be run on floors. If other orientations are desired, one must select illuminance from the normal cloud rendering service to simulate other surfaces. Before running the analysis, the model should be prepared for the simulation. This includes selecting the building location, defining
surface and glazing material properties, defining rooms and room parameters, and setting advanced simulation options. Five types of simulations can be run, which include illuminance, daylight autonomy, LEED 2009 IEQc8 opt 1, LEED v4 EQc7 opt 2, and solar access. Depending on the analysis type, other parameters can be configured, including lower and upper threshold, analysis plane height, sky model, and date and time. All analysis include floor level selection and analysis resolution. Once the analysis is finished running on the cloud, Revit downloads the results and displays them in a floorplan view. Results can be customized in terms of colors, labels, and legend. The results are displayed in vector form, so larger plans can be generated without any loss in quality. 5.2.3 Solar Radiation Analysis Solar radiation analysis measures the amount of solar radiation, in Wh/m2, kWh/m2, or BTU/ft2. The analysis supports mass surfaces and standard architectural elements, such as walls, floors and roofs. Detailed building elements, such as window glass and curtain wall mullions, are not supported at this time. The time period for the simulation can be set for several hours, days, or a full annual simulation. The analysis is also capable of measuring potential output of photovoltaic panels over one year, depending on the panel efficiency, electricity cost, and payback period. The analysis grid size can be adjusted before the simulations are run locally on the user’s computer. Results are displayed similar to the lighting analysis; as a colored surface or labeled points. Values are shown according to cumulative, peak, or average insolation, and the results can be exported to a .csv file. Results cannot be saved after a new simulation has been performed, so the data has to be recorded, or screenshots saved of the desired analysis surfaces.
5.3 Revit and Sefaira
Sefaira can be used to perform energy and daylight analysis of the Revit model. Before analysis is run, the building and weather location should be set. Sefaira uses .epw files for the historic weather data files, depending on the building location. When viewed in the Sefaira web-based application, users can also upload custom .epw files. During the geometry preparation stage, detailed building elements are assigned thermal conductance values, space use loads and schedules are defined, and mechanical systems specified. Model properties can be set
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to match an existing baseline, such as ASHRAE 90.1, or customized. The building type should also be specified in this step, which affects scheduling and building loads if using an existing baseline. In the analysis preparation stage, the building location is defined, which is used to find the nearest weather location. The location or weather file can be changed later in the web-based interface. If running an energy analysis, the simulation engine and units can be selected. If running a daylight analysis, the units (LUX or FC) and number of ambient bounces for Radiance can be selected. In the analysis simulation stage, the model is uploaded to the cloud, and either run through EnergyPlus for energy analysis, or Radiance for daylight analysis. When using the real-time analysis mode, results are brought back and visualized within the plugin. If the web-based application is used, energy results are displayed in a tabular format, with specific results available for every zone of the building.
5.3.1 Energy Analysis Sefaira uses the detailed Revit model as geometry input for the energy analysis. The plugin is not capable of altering any of the geometry before it is uploaded to the cloud if using the web-based application for analysis. When using the application, Table 3 lists geometry modifying parameters that can be used to override the existing geometry. The geometry, space use and systems modifying parameters are limiting or nonexistent when the plugin is being used in the real time analysis mode. Sefaira’s own engine for energy analysis, Fulcrum, is the only option for real time analysis. However, for full control over the model, the web-based application must be used. 5.3.2 Daylight Analysis Sefaira uses Radiance and DAYSIM engines for daylight analysis. When an analysis is run locally, the model is uploaded to the cloud, where simulations are run. They are then imported back to the model when completed. Daylight analysis options include units (FC or LUX), and the number of ambient bounces for Radiance (2, 3 or
Table 3: Parametric options for energy analysis in Sefaira.
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Geometry Modifying
Zoning (One zone/floor, perimeter/core - perimeter depth, one zone/room) Window to Wall Ratio (whole building or per orientation) Shading: Horizontal, vertical and automated blinds/shades (whole building or per orientation) Building orientation
Material Modifying
Facade U-Value and SHGC Wall assembly type and R-Value Floor finish and ground floor R-Value Infiltration type and value Roof glazing U-Value and SHGC Roof type and R-Value
Space Use
Building type: healthcare, laboratory, office, residential, retail and school. Design loads, ventilation and outside air, design temperatures, HVAC schedule, annual diversity factors and day schedules.
Systems Modifying
HVAC system type: air-side and water-side equipment Natural ventilation Photovoltaic system
Simulation Modifying
Energy baseline (ASHRAE 90.1) Weather location
Integration of Parametric Design Methods and Building Performance Simulations
Table 4: Daylight analysis types in Sefaira. Date & Time
Point in time analysis that is useful for best and worst case scenarios.
Annual Sun Exposure
Percentage of floor area above 1000 LUX for at least 250 occupied hours per year, and a lower threshold that can be set according to the building type.
Overlit & Underlit
Overlit: Over 1000 LUX of direct light for more than 250 occupied hours per year. Underlit: Less than 300 LUX for more than 50% of occupied hours.
Daylight Factor
Ratio of interior and exterior illuminance under an overcast sky.
Direct Sunlight
Shows where and for how many hours an area receives direct sunlight.
4). Sefaira makes a set of assumptions for the material reflectance values. All opaque materials are assumed to be a plastic with a matte finish, smooth surface, with an RGB reflectance value of 0.4. Glazing is assumed to have a matte finish with a smooth surface. The transmissive specularity value is 1, meaning that it does not diffuse light. The RGB reflectance value is .8841, but the visible transmittance value is defined in the Sefaira properties menu. The ground is automatically added within DAYSIM, and has a reflectance of 0.2. Except for the daylight factor analysis, all simulations use the Perez All-Weather Sky. The size of the analysis grid is based off of the size of the model, but the work plane height is customizable. These assumptions cannot be edited, but that functionality is planned for later releases of Sefaira.
6.0 EVALUATION OF THE FRAMEWORK AND CASE STUDY 6.1 Overview of the Case Study
To evaluate the previously discussed framework for integrated parametric modeling and building performance analysis, a prototype building located in the financial district of Boston was modeled (Figure 6). The evaluated workflows included Honeybee and Ladybug (Rhino 3D), Insight 360 (Revit) and Sefaira (Revit) software applications. The building is divided into a low-rise portion of five stories, and a high-rise portion of 15 stories. Floor-to-floor height was 12 ft (3.7 m), with a total height of 240 ft (73.2 m). The perimeter zone depth was 48 ft (14.6 m). The surrounding buildings were modeled from GIS data, and were included in the site model. Figure 6b shows Rhino model with surrounding buildings, and Figure 6c shows Revit model. Energy, solar radiation, and daylight analysis were run for the case study building using the three investigated workflows.
Floor plans with overlaid results can be exported out as images, although the resolution of the images is only moderate. In addition, there are no options to display the actual illuminance values over the floor plans, so it can be hard to compare different results when focusing on a specific illuminance value at a specific location. There are a variety of analyses that can be run, described in Table 4.
(a)
(b)
(c)
Figure 6: a) Case study site outlined in red; b) Rhino model with surrounding buildings; and c) Revit model with surrounding buildings.
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6.2 Application of The Framework and Simulation Methods 6.2.1 Ladybug and Honeybee Figure 7 shows the Grasshopper definition used for the analysis and simulations for the case study building within Rhino. Due to the graphic nature of Grasshopper,
Figure 7: Grasshopper definition used for the analysis.
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the organization of components is vital for understanding the user’s own definition. As more components and connections are added to the canvas, the file becomes increasingly visually complex. The components are divided into four stages: geometry preparation, analysis preparation, analysis simulation and visualization of results within the same environment.
Integration of Parametric Design Methods and Building Performance Simulations
6.2.2 Insight 360 Two Revit models were built to simulate the same conditions that were set up in the Rhino model, primarily for the lighting analysis. One contained individual windows to simulate window-to-wall (WWR) ratios between 20 percent and 50 percent, while one large window per facade orientation was needed for a WWR ratio of 80 percent. The Insight created from the energy model used the individual window model, as the cloud is able to simulate a variety of WWR ratios independent of what is modeled in Revit. The solar radiation analysis model utilized the massing model, as radiation results were
desired for multiple facades, and not detailed elements. For energy simulations, it is important to continually record the whole building EUI as the design factors are adjusted, as the built-in model history tool is not comprehensive enough for this task. For daylight and solar radiation analysis, it is helpful to save images of the results for easy viewing at later stages, as lighting results take a few steps to reimport back into the model, and radiation results are not saved. Figure 8 shows model of the case study building within Insight 360 interface, which includes EUI value, model history, and building orientation.
Figure 8: Insight 360 Dashboard: 3D model, EUI, model history and building orientation factor are shown.
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6.2.3 Sefaira Sefaira is able to use the same Revit model as Insight 360, with some changes. The total number of glazing planes cannot exceed 1,500 due to EnergyPlus constraints. The window design for WWR of 30 percent and 50 percent had to be changed to one window per orientation, instead of multiple windows, so that Sefaira could process and run the analysis. Figure 9 shows typical output within web-based application interface. Figure 10 shows customizable options for displaying specific simulations results, including detailed HVAC
loads, energy use and costs, thermal comfort parameters, emissions, etc. If the real time analysis is used, the window changes according to the analysis type. When energy and daylight analysis are run together, the window is similar to Figure 11. Thermal gains and losses are displayed relative to their impact on the building’s energy consumption, and the overall EUI. To the right, daylight results are overlaid on their respective floor plates.
Figure 9: Typical Sefaira energy analysis run within the web-based application.
Figure 10: Simulation result data available within Sefaira web-based application.
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Integration of Parametric Design Methods and Building Performance Simulations
Figure 11 Real time energy and daylight analysis window in Sefaira.
6.3 Simulation Results: Ladybug and Honeybee (Rhino)
6.3.1 Energy Analysis Results Honeybee exports model geometry and settings to EnergyPlus, which performs the energy simulation. The simulation options that were parametrically controlled in Honeybee included glazing ratio, temperature set-
points, wall and roof R-values, glazing U-values, SHGC, and Vt, infiltration rates, HVAC systems, and lighting power density. Since the building geometry was complex, each run ranged from taking 20 to 60 minutes to complete. The number of windows had a direct impact on simulation time. Table 5 shows properties of investigated models and respective EUI results.
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Table 5: Energy simulation results (Honeybee). Run Name
Variables
EUI (kBTU/ft2, kWh/m2)
Baseline
50% WWR VAV w/ Reheat, 72°F/78°F Set-points Wall: R-19, Roof: R-30 Window: 0.45/0.38/0.42 (U/SHGC/VT) Infiltration: 0.8 ACH Lighting power density: 1 W/ft2 12/6 Schedule
68.7 (216.7)
Low WWR
30% WWR
65 (205.1)
High WWR
80% WWR
75.4 (237.8)
Setpoints
68°F/82°F Set-points
51 (161)
Higher R-Values
Wall: R-40, Roof: R-60 Window: 0.2/0.38/0.42 (U/SHGC/VT)
58.9 (185.7)
Infiltration
Infiltration: 0.2 ACH
41 (129.5)
Lighting Power Density
Lighting power density: 0.3 W/ft
67.3 (212.4)
HVAC Alternate
Fan coil units + DOAS
40.4 (127.3)
Best Case
50% WWR, Wall: R-40, Roof: R-60 Window: 0.2/0.38/0.42 (U/SHGC/VT) Fan coil units + DOAS, 68°F/82°F Set-points Infiltration: 0.2 ACH Lighting power density: 0.3 W/ft2
14.4 (45.3)
2
6.3.2 Radiation Analysis Results The Ladybug Radiation Analysis Tool uses the location of the sun for every hour of the year to determine how much radiation the exterior surfaces receive. Surrounding buildings are taken into account during this analy-
sis. Simulation options that were parametrically controlled included analysis period, sky type, grid size, grid distance off surface, and legend (low and high bound). Results are shown in Figures 12a and 12b.
Figure 12: a) Cumulative radiation analysis, June 21; b) Cumulative radiation analysis, December 21.
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Integration of Parametric Design Methods and Building Performance Simulations
6.3.3 Daylight Analysis results Honeybee exports model geometry and settings to Radiance, which performs daylight simulations. The simulation options that were parametrically controlled in Honeybee included analysis period, sky type, grid
size and distance off surface, radiance rendering parameters, and simulation type (illuminance, radiation, luminance). Typical results are shown in Figures 13a and 13b.
Figure 13: a) Daylight analysis, June 21, 20% WWR; and b) Daylight analysis, June 21, 80% WWR.
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6.4 Simulation Results: Insight 360 (Revit)
6.4.1 Energy Analysis Results Revit exports the model geometry and settings to the cloud, where the simulations are run through EnergyPlus. Alternative design factors can be simulated by varying building loads, model geometry and systems. Table 6 includes a series of options that represent all the alternate design factors that were simulated. The results are then displayed within the Insight 360 interface. One simulation run (baseline and design factors) are iden-
tified as one Insight. The 3D energy model is shown, along with the EUI of the building. A slider allows the user to change design factors, and see how the EUI is affected in real time, without having to run another simulation. The sliders can subsequently be adjusted to find the best EUI according to the factors selected. It can take anywhere from 30 minutes to a few hours to run an Insight, depending on the cloud server load and account type. Results can be displayed in terms of EUI or annual costs.
Table 6: Insight 360 energy simulation results.
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Run Name
Variables
EUI (kBTU/ft2, kWh/m2)
Baseline
50% WWR, no shading ASHRAE VAV Wall: R-19, Roof: R-30, Slab: R-23 Window: Double Low-E Infiltration Rate: 0.8 ACH, Lighting: 1.1 W/ft2 Daylighting: None Building Type: Office, Schedule: 12/6
79.1 (225)
Low WWR
30% WWR
77.6 (245)
High WWR
80% WWR
86 (271)
Higher R-Values
Wall: R-38, Roof: R-60, Slab: R-23 Windows: Triple Low-E
76.4 (241)
Infiltration
Infiltration Rate: 0.17 ACH
73.7 (233)
Daylighting
Daylighting and Occupancy Controls Horizontal shading south and west (Âź window height) Lighting: 0.3 W/ ft2
73.7 (232)
HVAC
High Efficiency VAV
69.2 (218)
HVAC Alternate
High Efficiency Package System
45.7 (144)
Schedule
12/5
77.7 (243)
Best Case
30% WWR Horizontal shading south and west (Âź window height) HVAC: High Efficiency Package System Wall: R-38, Roof: R-60, Slab: R-23 Windows: Triple Low-E Daylighting and Occupancy Controls Lighting: 0.3 W/ ft2 Schedule: 12/5
27.7 (87.5)
Integration of Parametric Design Methods and Building Performance Simulations
6.4.2 Radiation Analysis Results The solar analysis tool was used to study the south facade of the building for average, cumulative and peak insolation. Figure 14 shows cumulative insolation val-
ues for different times of the year, where the effects of adjacent buildings can be seen on the low rise portion of the building.
Figure 14: a) Cumulative insolation, March 21; b) Cumulative insolation, June 21; and c) Cumulative insolation, December 21.
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6.4.3 Daylight Analysis Results Insight 360 uploads the Revit model to the cloud, where daylight simulations are run. The 10th floor was simulated according to the LEED v4 EQc7 opt 2 analysis type, which measures the percentage of floor area that
is between 300 and 3,000 LUX, at 9am and 3pm on the equinox. Figure 15 shows the results. It is also possible to perform threshold analysis to determine percentage of the floor area that meets the LEED requirements. Table 7 shows the results.
Figure 15: a) Daylight analysis, September 21, 3 PM with 20% WWR; and b) Daylight analysis, September 21, 3 PM with 80% WWR
Table 7: 10th floor daylight threshold lighting analysis results. 9am - September 21
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3pm - September 21
within
above
below
within
above
below
20% WWR
38%
1%
60%
39%
3%
59%
80% WWR
81%
16%
3%
73%
21%
6%
Integration of Parametric Design Methods and Building Performance Simulations
6.5 Simulation Results: Sefaira (Revit)
6.5.1 Energy Analysis Results When an energy analysis is run, the model is uploaded to the cloud, where it is run through EnergyPlus. The results are either displayed in the web-based application,
or within the plugin in Revit. On average, it takes three to five minutes for each energy analysis run. Runs utilizing thermal comfort or natural ventilation factors take significantly longer to process. Runs can be cloned to use as alternates with different design options within the same model. Results are shown in Table 8.
Table 8: Sefaira energy simulation results. Run Name
Variables
EUI (kBTU/ft2, kWh/m2)
Baseline
50% WWR, no shading VAV - Return Air Package (System 5/6) Wall: R-19, Roof: R-30, Slab: R-23 Window: 0.45/0.38 (U/SHGC) Infiltration Rate: 0.8 ACH, Lighting: 1.1 W/SF Design Temperatures: 72/78 Building Type: Office, Schedule: 12/6: 7am-7pm
92 (290)
Low WWR
30% WWR
92 (290)
High WWR
80% WWR
94 (296)
Higher R-Values
Wall: R-40, Roof: R-60, Slab: R-23 Window: 0.2/0.38 (U/SHGC)
86 (271)
Infiltration
Infiltration Rate: 0.17 ACH
47 (148)
Shading
1ft deep exterior horizontal shades Internal blinds
94 (296)
HVAC Option 1
Fan coil units and central plant
77 (243)
HVAC Option 2
Radiant floor
57 (180)
HVAC Option 3
Active chilled beams
80 (252)
Schedule
12/5: 7am-7pm
93 (293)
Best Case
50% WWR, no shading Wall: R-40, Roof: R-60, Slab: R-23 Window: 0.2/0.38 (U/SHGC) Infiltration Rate: 0.17 ACH HVAC: Radiant floor Schedule: 12/5: 7am-7pm
24 (76)
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6.5.2 Daylight Analysis Results Sefaira uploads the model to the cloud, where Radiance and DAYSIM are used to perform daylight simulations. The calculation time is fast, and results are displayed within the model, as seen in Figures 16 to 18. If a daylight analysis is run and has already been simulated in the past, the results appear within seconds within the model. Results cannot be saved in the daylight analysis mode, but if they have been previously run, the wait to retrieve results is negligible.
Figure 18: Overlit area (1000 LUX) for more than 250 occupied hours per year, and underlit (300 LUX), for more than 50 percent of occupied hours.
6.6 Discussion of Results
All simulations followed similar procedures for geometry preparation and visualization of results. After the base geometry was completed, geometry and simulation modifying parameters were defined, and building zones were run through the simulation engines. Data results were either viewed locally, or in the cloud. Figure 16: Percentage of occupied hours where illuminance is at least 300 LUX, measured at 2.79 feet above the floor plate.
Figure 17: Daylight factor and percentage of floor area, measured at 2.79 feet above the floor plate.
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Rhino, utilizing Grasshopper, Ladybug and Honeybee, offers significant customizability for parametric control of geometry, while also offering many different simulation types. The parametric nature of Grasshopper means that there is an infinite number of forms and strategies that can be investigated. One major drawback of this workflow is that the components have to be configured before the initial use. Once they are configured, the Grasshopper definitions can be repeatedly used on the same or different projects. Another drawback is the learning curve required to use the software. While other software programs utilize a series of dialog boxes to configure the parametric and simulation options, this workflow requires the user to set up all the components before visualizing the results. It is possible to obtain preconfigured Grasshopper files for Ladybug and Honeybee analysis types, but if the user does not fully understand how they are set up, configuring them to suit their own project is difficult. Additionally, if component inputs are not defined properly, or the wrong components are used, warnings and errors will appear in the simulations. Some of these are easy to fix, while
Integration of Parametric Design Methods and Building Performance Simulations
other require a deeper understanding of how the framework operates. Revit is the widely adopted BIM software in architecture and construction industry. Insight 360 is easy to download and install, and the learning curve is fairly light. With the energy and daylight simulations being run in the cloud, the results are generated relatively quickly while the user can continue working in the host software in the meantime. The customizability of the daylight and solar radiation analysis is adequate, but the energy simulation parameters are limiting. Further, the lack of parametric tools to generate different WWRs or shading methods based on building orientation is a shortcoming. Additionally, the parametrically generated energy model cannot be used for daylight or solar radiation analysis. Sefaira is easy to install, as well as to use. The organization of energy analysis runs within the web-based application makes tracking model changes straightforward. Sefaira’s use of the cloud for simulations decreases the time necessary for calculations. The daylight analysis tool is excellent for analyzing the overall light levels and
daylighting metrics, although its capabilities for showing specific values on a floor plan are limiting. Additionally, the plugin lacks support for solar radiation analysis. Although the accuracy of the simulations was not the focus of this research, the energy analysis results were compared to each other, as shown in Tables 9 and 10. All three program use the same engine, EnergyPlus, for the energy calculations. The only differences between the three tools are variation in inputs and geometry discrepancies between Rhino and Revit. The EUI results from Honeybee and Insight 360 were compared to Sefaira, and the differences were expressed as a percentage. Insight 360 and Sefaira results were the closest to each other, with the Insight 360 baseline run coming in 16 percent lower than the Sefaira run, and the best case run came in 15 percent higher. The Honeybee baseline run came in 34 percent lower, while the best run came in 66 percent lower. The parameters for daylight and occupancy controls cannot be set in Sefaira. Insight 360 does not allow for control over the temperature set-points. Ladybug and Honeybee have support for both of these variables, but daylight and occupancy sensors were not simulated. All three programs used
Table 9: Energy baseline runs comparison (Honeybee, Insight 360 and Sefaira). Run Name
EUI (kBTU/ft2, kWh/m2)
Difference (%)
Annual Energy ($/ft2, $/m2)
Difference (%)
Honeybee Baseline
68.71 (216.74)
-34%
$2.69 ($29)
+39%
Insight 360 Baseline
79.1 (225)
-16%
$2.57 ($27.7)
+33%
92 (290)
0%
$1.93 ($20.84)
0%
Annual Energy ($/ft2, $/m2)
Difference (%)
Sefaira Baselinet
Table 10: Energy best case runs comparison (Honeybee, Insight 360 and Sefaira). Run Name
EUI (kBTU/ft2, kWh/m2)
Difference (%)
Honeybee Best Case
14.4 (45.3)
-66%
$.4 ($4.38)
-120%
Insight 360 Best Case
27.7 (87.5)
+15%
$.96 ($10.3)
+9%
24 (76)
0%
$.88 ($9.42)
0%
Sefaira Best Case
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an electricity rate of $.1437 per kWh. Honeybee and Sefaira used a gas rate of $.04 per kWh, while Insight 360 used $3.178 per cubic meter. The results from the daylight analysis are difficult to compare since detailed illuminance values were not collected for Sefaira. In addition, the reflectance values of floors, walls and ceilings cannot be set in Sefaira, so the illuminance values would not match even though the simulation engine is the same. Further testing would be needed to compare the accuracy of the daylighting analysis for Honeybee, Insight 360 and Sefaira.
7.0 CONCLUSION
The objective of this research was to investigate methods for integrating parametric design with building performance analysis. An ideal framework for integration of parametric and performance analysis procedures was developed. Then, the framework was tested using existing software applications, including BIM-based and non-BIM design software, parametric design and building performance analysis applications. Three different workflows were tested, which integrate building performance analysis applications. Specifically, Honeybee and Ladybug (for Rhino 3D) were evaluated as a non-BIM workflow, while Insight 360 (for Revit) and Sefaira (for Revit) were evaluated as BIM-based methodologies. A case study building was used to test and evaluate the workflows, interoperability, modeling strategies and results. Three different building performance aspects were analyzed for each workflow: 1) energy analysis, 2) solar radiation analysis, and 3) daylighting. For energy analysis, parametric control of window-to-wall (WTW) ratio, thermal resistance of exterior walls and roof, HVAC system types, infiltration and lighting power density (LPD) was tested. Energy Usage Intensity (EUI) was used as a performance benchmark to compare results of investigated scenarios. For solar radiation analysis, average, cumulative and peak insolation were calculated for building’s exterior surfaces, where the effect of the adjacent buildings was taken into account. For daylighting analysis, parametric control of analysis period, sky type, analysis grid, rendering parameters and simulation type (illuminance, radiation, luminance) was tested. Simulation results from energy analysis, solar radiation and daylight simulations were recorded and analyzed, and are discussed in this article in detail. However, each evaluated workflow has certain benefits and drawbacks.
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The framework applied to Rhino, Grasshopper, Ladybug and Honeybee offers a lot of options and customizability of the parametric and simulation options. The lack of BIM integration in this framework is a drawback, which means that many designers may use it for conceptual and/or schematic design, but will migrate to a BIM-based software for schematic and design development phases. Insight 360 is able to integrate building performance simulations within a BIM environment, while ensuring that the tools are easy to use. The free plugin is offset by the costs to run simulations on the cloud, although they are run much faster than if they were done locally. Since Insight 360 has only been available for a short time, the functionality of the tool has its limits. While heating and cooling loads can be visualized in the model and downloaded as a csv file, an in-depth breakdown of energy usage and zones is not easy to see visually. Sefaira takes the customizability of Ladybug and Honeybee and the accuracy of Insight 360 and integrates it into a BIM environment. The energy and lighting analysis can be simulated quickly, and the variety of visualization methods makes understanding the data easy. The daylighting simulations have a few drawbacks, which include lack of support for detailed illuminance values at specific points on the floor plan, an analysis grid that cannot be adjusted by the user, and the inability to modify reflectance values of materials. Solar radiation analysis is also not included in Sefaira. All three programs use the same simulation engine for energy analysis, so differences in the results are due to geometry and input variations. Not all parameters can be changed in each program. Additionally, the customizability of Ladybug, Honeybee and Sefaira in terms of building schedules, loads and HVAC system settings can produce results with greatly different values if the user is not certain about the input values. The results show a promising course for integrating parametric design with building performance simulations. This would allow designers to evaluate the effects of design decisions earlier in the design stages. Moreover, by integrating the capabilities of parametric design and building performance simulations, multiple design variables can be tested rapidly, creating a more cohesive and effective design process.
Integration of Parametric Design Methods and Building Performance Simulations
REFERENCES
[1] Aksamija, A., (2013). “Building Simulations and High-Performance Buildings Research: Use of Building Information Modeling (BIM) for Integrated Design and Analysis”, Perkins and Will Research Journal, Vol. 5, No. 1, pp. 19-38.
[10] Sadeghipour Roudsari, M., and Pak, M., (2013). “Ladybug: A Parametric Environmental Plugin for Grasshopper to Help Designers Create an Environmentally-Conscious Design”, Proceedings of the 13th International IBPSA Conference, Lyon.
[2] Bazjanac, V., (2008). “IFC BIM-based Methodology for Semi-automated Building Energy Performance Simulation”, Proceedings of the CIB-W78 25th International Conference on Information Technology in Construction, Santiago, Chile, pp. 292–299.
[11] Roth, A., (2016). “Autodesk Upgrades Insight360 with EnergyPlus Annual Energy Simulations”, U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, Retrieved on 02/2017 from https://energy.gov/eere/buildings/articles/autodeskupgrades-insight360-energyplus-annual-energy-simulations.
[3] Schlueter, A., and Thesseling, F., (2009). “Building Information Model Based Energy/Exergy Performance Assessment in Early Design Stages”, Automation in Construction, Vol. 18, No. 2, pp. 153–163. [4] Pratt, K., and Bosworth, D., (2011). “A Method for the Design and Analysis of Parametric Building Energy Models”, Proceedings of Building Simulation: 12th Conference of International Building Performance Simulation Association, Sydney. [5] Elnimeiri, M., and Nicknam, M., (2011). “A Design Optimization Workflow for Tall Buildings Using Parametric Algorithm”, Proceedings of the Council on Tall Buildings and Urban Habitat (CTBUH) Seoul Conference, pp. 561-569. [6] Rahmani, M., Zarrinmehr, S., and Yan, W., (2013). “Towards BIM-Based Parametric Building Energy Performance Optimization”, Proceedings of the Association for Computer-Aided Design in Architecture (ACADIA): Adaptive Architecture, Ontario. [7] Oxman, R., (2008). “Performance-based Design: Current Practices and Research Issues”, International Journal of Architectural Computing, Vol. 6, No. 1, pp. 1-17. [8] Turrin, M., von Buelow, P., and Stouffs, R., (2011). “Design Explorations of Performance Driven Geometry in Architectural Design using Parametric Modeling and Genetic Algorithms”, Advanced Engineering Informatics, Vol. 25, No. 4, pp. 656-675. [9] Negendahl, K., (2015). “Building Performance Simulation in the Early Design Stage: An Introduction to Integrated Dynamic Models”, Automation in Construction, Vol. 54, pp. 39-53.
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03.
SPATIAL AND MOTIVATIONAL PROGRAMMING IN OFFICE ENVIRONMENTS: Comparison of Co-Working to Other Types of Commercial Spaces Tina Nguyen, LEED GA, tina.nguyen@perkinswill.com ABSTRACT The well-being, productivity and engagement of employees in commercial buildings are important for creating contemporary working environments. This research article explores different working environments by applying two research methods to evaluate space use—motivational and spatial programming. Five case studies were used to explore the commonality and differences of spatial design and goals based on different industries. The industries that were investigated include a global co-working office, design offices, technology office, and research lab/science technology offices. It was determined that all investigated types of commercial spaces benefit from a diverse layout. This allows the users to organize themselves according to their needs, which might vary for different types of office spaces. By using motivational and spatial programming, designers are able to consider users’ needs, and improve functionality of commercial office spaces. KEYWORDS: future workspace design, spatial-program, motivational program, co-shared workspace
1.0 INTRODUCTION
2.0 BACKGROUND RESEARCH
With a dramatic shift in the trends seen in the new workplace, progressing to address a more global and flexible work environment, the new workplace culture has evolved. Instead of working at a designated type of workspace, the market has more opportunities that accommodate many conveniences within a workplace environment. This has resulted in establishment of co-working office spaces, which allow for different subculture communities to exist in the same working environment. The companies service offerings are shaped around entrepreneurs, freelancers, startups, small businesses, and even large enterprises that need temporary workspace. The office environments can meet everyone’s needs. These commercial spaces allow members and users to connect through a communal and “open” workspace. This workplace model has caused a dramatic shift as it has the potential to maximize engagement, productivity, and well-being at a global community scale.
Driven by cost effectiveness and the trend to build for collaboration, open offices have gained steady momentum, becoming the rule rather than the exception. Open offices have also transitioned towards fewer area per person (225 sf/person in 2010 to 176 sf/person in 2012 to 151 sf/person in 2017)2. With this theoretical cost efficiency, it is difficult to design a mixed space for clients to maximize worker efficiency, as well as worker space. The move towards open offices have encountered other concerns as well. As connectivity technology integrates with work life, there are more distractions brought into the office, smartphones and ease of access to the internet being the primary contributors. On average, office workers lost 28 percent of their productive time due to interruptions and distractions3. Employees are more
The purpose of this research was to understand the shift in workplace culture and the patterns in workspace design based on industry types. For this study we evaluated three types of workspaces; the design office workplace, the technology office workplace and the research environment workplace.
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Before the 1950s, most open offices consisted of regular rows of desks where workers completed repetitive tasks. In the 1950s, the Quickborner team developed the office landscape, a method that used furniture, screens and plants to create working group areas using organic geometry. As office work becomes more knowledge-based, many companies have moved towards a hybrid design that include offices, open workstations, and group workstations1.
Spatial and Motivational Programming in Office Environments
likely to have to work extended hours and/or find a quiet space in the office in which they can concentrate and focus, such as unused conference or phone rooms4,5. The users’ needs are changing over time, and generational variations are affecting workforce. In 2015, millennials overtook baby boomers to become the largest generational cohort in the workplace, and by 2020 millennials will make up over 50 percent of the entire workforce6. Some industries benefit from using a traditional, or enclosed workspace, and others from a more flexible, or open shared workspace. The key element in selecting and designing office spaces is to understand the motivation and goals from the users, companies, and industries.
3.0 RESEARCH GOALS AND TERMINOLOGY
The focus of this study was to explore relationships between workspace design, spatial programming and motivational programming. Literature review was conducted on workspace design, and its’ effects on productivity. The primary goal was to use the two specific methods: spatial programming and motivational programming. A comparison between these two methods will be made in order to understand the relationship between spatial programming and motivational programming. These methods allow to understand efficiencies of space allocations. During literature review, it became evident that most studies referred to the various ways of partitioning space in differing terms based on authors’ preferences. The most fitting terms that were encountered included “Activity Based Workstations versus Traditional Layouts”5 and “Collaborative Workspace vs. Focused Workspace”7. Therefore, these following definitions outline characteristics of different workspaces: • Open Space: any unenclosed space in which collaboration, particularly unprompted collaboration, may occur. • Enclosed Space: any enclosed space defined by walls or space referred to generally as traditional private workspace. • Shared Space: any space in which collaboration or any form of interaction. • Non-Shared Space: any space that is an independent working space, solely used by a particular individual or for a specific use. An open office layout with desks instead of cubicles is
an example of an open space. A conference room in which a team might meet to work on a project is an example of a shared space. An open lounge could be either open or shared depending on its use. Employees eating lunch in the lounge would define it as an open space, but the same employees meeting in the same lounge for a discussion or a meeting would change its use to define it as a shared space.
3.1 Research Methodology
3.1.1 Spatial Programming Spatial programming identifies spaces that are enclosed-shared space, enclosed non-shared space, open-shared space, and open-non shared spaces based on general observations. Different spatial programs were identified based on the spatial elements, as well as if they were shared vs. non-shared spaces. For the purpose of this study, the following descriptions outline how spaces have been defined: • Enclosed Shared Spaces: are defined as a space enclosed by walls and intended for shared occupancy only. They have to be reserved in advance via an online system, and prioritized on a first come first serve basis. (ex. conference room). • Enclosed Non-shared Spaces: are defined as a space enclosed by walls where only a single or particular occupancy is allowed. (ex. private office room). • Open Shared Spaces: are defined as spaces with no restricting walls, and open to the active circulation. These areas would be for shared occupancy (ex. team meeting tables and open lounge areas). • Open Non-Shared Spaces: are defined as spaces with no restricting walls, open to active circulation. These areas would be for single occupancy only. (ex. open workstations). 3.1.2 Motivational Programming Motivational Programming identifies spaces that enhance occupants’ activities, such as well-being, engagement, and productivity. For the purpose of this research, well-being addresses a participants’ personal comfort, and relates to their physical environment. Categories for consideration included: control, facility conditions/building maintenance, facility location, access to clinic or hospital, neighborhood character, spatial quality, spatial organization, space efficiency, shared offices, furniture/ergonomics, lighting, views, amenities, transportation, campus connectivity, culture, generations and perception, and group iden-
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tity7. Spaces and amenities that supported these basic needs for personal use during working hours were evaluated. Engagement refers to levels of enthusiasm or emotional investment that an employee has in terms of commitment to the organization’s values and goals7. Engagement is evaluated through these categories: process awareness, comparative cases, move management and communication, building use protocols, adaptations, and working elsewhere. In this research, we measured engagement through connectivity and collaborative spaces that facilitate communication. Across the case studies, productivity and performance were measured by the ability to do work within the work environment6, 7. The following categories were used to measure productivity in office environments: distraction, privacy, facility use, and administrative burden. In this research, observations were made to identify spaces that allow employees to work independently. In order to illustrate program elements that were associated with motivational programming elements, the following examples of spaces were recorded and observed:
• Well-Being program elements included open lounge, mother’s room, prayer or meditative spaces, bathrooms, or kitchen since these spaces relate to the basic biology of human function. Well-being spaces support basic needs, as well as emotional and mental wellness. • Engagement program elements included collaborative spaces, such as meeting rooms or conference rooms, which ranged from small to large scales. These spaces allow for connectivity and interaction with fellow co-workers. Classrooms and lecture halls could also serve as engagement spaces. • Productivity program elements included independent workspaces that were mostly nonshared (but have the ability to be shared). The key elements that were considered were those associated with productivity and efficiency in the workplace. Figure 1 shows relationships between spatial and motivational programming, and how these methods have been used to evaluate characteristics of these case studies.
SHARED PRODUCTIVITY
ENCLOSED
OPEN
NON-SHARED
Figure 1: Methodology for spatial and motivational programming.
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ENGAGEMENT
WELL-BEING
Spatial and Motivational Programming in Office Environments
3.2 Methodology Application and Data Collection
In analyzing the case studies, the first step was to define the organizations’ goals and values, which influenced their spatial programming and thereby affected the results, which were driven by the motivational programming methodology. The culture of working environments were also analyzed, since it impacts the fundamental ways how employees use the space and interact within their office environments. Then, data was collected based on spatial and motivational programming requirements explained is Section 1.3. For spatial programming, the following data was recorded: • Across all case studies, we recorded the area of each spatial use from each floor plan (for– enclosed shared, enclosed non-shared, open non-shared and open shared spaces). • Typical enclosed non-shared space, defined as private office space. • Typical enclosed shared spaces, defined as phone rooms, printing rooms, and bathrooms. • Typical open non-shared spaces, defined as office space, and/or independent workspaces. • Typical open shared spaces, defined as open kitchen, meeting tables, gallery halls, and workspace meeting tables. For motivational programming, the following data was recorded: • Across all case studies, we recorded the area of each motivational use from each floor plan. • Typical productivity space, defined as independent workspaces (non-shared spaces). • Typical engagement space, defined as conference rooms, meeting tables, gallery hall, open work areas (shared spaces). • Typical well-being, defined as lounges, kitchens, and phone rooms (shared, open and enclosed spaces).
4.0 CASE STUDIES 4.1 Introduction to Case Studies
The research team applied spatial and motivational programming as a methodology to analyze a number of different case studies:
Global Workspace Co-Working Space Design Office Workspace Perkins and Will Seattle Perkins and Will Minneapolis Tech Office Workspace Social Media Company Research Workspace Research & Healthcare Facility 4.1.1 Global Workspace: Co-Working Space This case study is an open shared office workspace that identifies building a global community as their primary goal. The case study office space that was analyzed in this research study was 11,800 sf. The work culture utilizes shared resources (equipment, services, etc.), and connecting with other professionals in different fields. It provides the basic amenities for commercial office space, including a shared kitchen, open lounge space, and conference rooms. Transparency is highly emphasized in the work shared office spaces. There is no private partitioning unless requested. Enclosed non-shared space makes up 8,971 sf (63 percent) of the space as primarily offices. Open-shared space makes up 3,657 sf (26 percent) of office space. These programs are open kitchens, hot desks, open lounges, and open workspace. Enclosed shared space makes up 1,655 sf (11 percent) of office space. These programs includes conference rooms, phone rooms, printing rooms, and bathrooms. In combination, shared spaces makes up 37 percent of office space. This shows how shared space highlights interaction and a sense of a global community regardless if it is enclosed or open workspace. Figure 2 shows results of spatial programming analysis. There are 10,290 sf (72 percent) of office space used for productivity-based activities. Second, there are 3,099 sf (22 percent) of office space used for well-being activities; programs include open-shared lounges, open shared kitchens, enclosed shared phone rooms, and enclosed shared bathrooms. These programs qualify for well-being due to human needs, emotional
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SPATIAL PROGRAMMING 0% 11%
Enclosed Shared Space: 1,655 sf Enclosed Non-Shared Space: 8,971 sf Open-Shared Space: 3,657 sf Open Non-Shared Space: 0 sf
26%
RESEARCH FINDINGS
63%
SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 1,655 11%
ENCLOSED-NON-SHARED 8,971 63%
Conference Room x3
Private Office Space
Kitchen
Phone Room x 8
Hot Desks
(Small, Medium, Large)
Printing Room
Lounge
Bathrooms
Workspace
Figure 2: Results of spatial programming for a co-working office.
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ENCLOSED-SHARED
OPEN-SHARED 3,657 26%
ENCLOSED-NON-SHARED
OPEN-SHARED
OPEN-NON-SHARED 0 0%
OPEN-NON SHARED
Spatial and Motivational Programming in Office Environments
MOTIVATIONAL PROGRAMMING
Productivity Space: 10,290 sf Engagement Space: 894 sf Well-Being Space: 3,099 sf
22%
6%
RESEARCH FINDINGS
72%
MOTIVATIONAL PROGRAMMING TABLE PRODUCTIVITY SPACE 10,290 72%
PROGRAMS
Enclosed Non-Shared Workspace
PRODUCTIVITY SPACE
ENGAGEMENT SPACE 894 6%
ENGAGEMENT SPACE
WELL-BEING SPACE
WELL-BEING SPACE 3,099 22%
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Open-shared Workspace
Open-Shared Kitchen
Open-shared Meeting Space
Enclosed-Shared Phone Room Enclosed-Shared Bathrooms
Figure 3: Results of motivational programming for a co-working office.
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well-being, and individual’s preference in use of space. Lastly, there are 894 sf (6 percent) office space dedicated to engagement space. These programs include enclosed-shared conference rooms, open-shared workspace, and open shared meeting space. These spaces allow building a global community and connecting with professionals from different fields. Figure 3 shows results of motivational programming analysis.
Results indicate that 26 percent of office space is open space illustrating transparency and connectivity. 74 percent of office space is enclosed, showing mostly collaboration and a community that is collective based on office goals. 37 percent of office space is shared, motivating collaboration, integration and connectivity goals. 55 percent of office space is non-shared, but it is equally as important as shared because these nonshared spaces provide productivity. Figure 4 shows combined results.
Figure 4: Combined results of spatial and motivational programming for a co-working office.
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Spatial and Motivational Programming in Office Environments
4.1.2 Design Office Workspace: Perkins and Will Seattle Perkins and Will’s Seattle office, shown in Figure 5, is 12,300 sf. The program goals for this architectural office space included adaptability, introduction of studio workspaces, improved connectivity and collaboration. It primarily consists of open shared office spaces, collaborative spaces and studio work. As an architecture firm, the culture of the firm requires creative spaces, open spaces, transparency, collaboration, and connectivity. The firm identifies collaborative meetings are essential to the flow of ideas and creativity. Enclosed shared spaces, open shared spaces, and open non-shared spaces are prevalent in the office. Open non-shared space makes up 6,703 sf (52 percent) of the office space, defining the open independent workspaces. Open shared spaces are 3,541 sf (27 percent) of the office, with programs like reception, open kitchens, meeting tables, lounges, gallery halls and workspace meeting tables. Enclosed shared spaces make up 2,683 sf (21 percent) of the office space with the programmatic uses of conference rooms, phone rooms, printing rooms and bathrooms. Enclosed nonshared was not included in the program, indicating that there was no specific need for private use. Overall, 48 percent of the office utilizes shared workspace. Figure 5 shows results of spatial programming analysis.
There are 6,703 sf (52 percent) of space used for productivity, which include programs like independent open workspaces. There are 3,501 sf (28 percent) of office space for engagement spaces; programs include enclosed-shared conference rooms, open shared meeting table’s spaces for teams to collaborate, and open shared gallery hall space for a studio work and critique session. There are 2,240 sf (18 percent) of office space dedicated to well-being. These programs include open shared lounges, open shared kitchens, and an enclosed shared phone rooms. Figure 6 shows results of motivational programming analysis. Results indicate that 79 percent of office space is open space illustrating the transparency and connectivity culture of the office, while 21 percent of office space is enclosed, showing mostly collaboration and special use areas goals. Results also indicate that 48 percent of office space is shared, motivating collaboration, integration and connectivity goals, while 52 percent of office space is non-shared for productivity and efficiency. Figure 7 shows results of combined spatial and motivational programming analysis.
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Chart Title Enclosed Shared Space: 2,683 sf Enclosed Non-Shared Space: 0 sf Open-Shared Space: 3,541 sf Open Non-Shared Space: 6,703 sf
21%
0% 52%
RESEARCH FINDINGS
27%
SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 2,683 21%
ENCLOSED-NON-SHARED 0 0%
ENCLOSED-SHARED
OPEN-SHARED 3,541 27%
Conference Room x 5
Reception
Phone Room x 3
Kitchen
Printing Room
Meeting Tables
Bathrooms
Lounge
Printer Supply
Gallery Hall Workspace
Figure 5: Results of spatial programming for Perkins and Will Seattle office.
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ENCLOSED-NON-SHARED
OPEN-SHARED
OPEN-NON SHARED
OPEN-NON-SHARED 6,703 52%
Open Workspace
Spatial and Motivational Programming in Office Environments
18%
Productivity Space: 6,703 sf Engagement Space: 3,501 sf Well-Being Space: 2,240 sf
Chart Title
54%
28%
RESEARCH FINDINGS MOTIVATIONAL PROGRAMMING TABLE
PROGRAMS
PRODUCTIVITY SPACE 6,703 54%
PRODUCTIVITY SPACE
ENGAGEMENT SPACE 3,501 28%
ENGAGEMENT SPACE
WELL-BEING SPACE
WELL-BEING SPACE 2,240 18%
Open Non-Shared Workspace
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Enclosed-Shared Printer Supply
Open-shared Meeting Space
Open-Shared Kitchen
Open-Shared Gallery Hall Space
Enclosed-Shared Phone Room
Figure 6: Results of motivational programming for Perkins and Will Seattle office.
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Figure 7: Combined results of spatial and motivational programming for Perkins and Will Seattle office.
4.1.3 Design Office Workspace: Perkins and Will Minneapolis Perkins and Will’s Minneapolis office, seen in Figure 8, is 9,800 sf. This case study is primarily an open shared office space that emphasizes the connection within a community, freedom to work anywhere on a daily basis, allowing the office space to be adaptable. The office culture requires creative space for individuals who either engage in project discussion, or work independently at their desks. Collaborative meetings are essential to the flow of ideas and the space is intended to be completely flexible. The amount of shared space, both enclosed and open, is highly prized in this office (5,613 sf or 65 percent). The program also includes kitchens, material libraries, meeting tables, lounges and workspace. Enclosed shared space makes up 2,469 sf (28 percent) of the office space. These programs include conference rooms, phone rooms, printing rooms, and bathrooms. There are 618 sf (7 percent) of private office space. Almost 93 percent of the space is shared space. These shared spaces identify with the flexibility and adaptability required for daily use. Non shared space is limited as per the work culture of the office. Figure 8 shows results of the spatial programming analysis. There are 2,936 sf (32 percent) of space dedicated for productivity (open shared workspace). These shared spaces can be a hybrid of engagement and productivity
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use due to the change in seats and “work anywhere” concept. There are 3,739 sf (40 percent) of office space that is considered engagement space; programs include enclosed-shared conference rooms, open shared work space, and open shared meeting tables spaces for individuals or team meetings. These programs qualify for engagement due to connectivity, collaboration, and adaptability in work sessions. There are 2,621 sf (28 percent) of office space dedicated to well-being. These programs include open shared lounges for independent time or collaborative meetings, open shared kitchens for dining with flexibility in its use based on the individual’s choice and or open in a collaborative manner, and an enclosed shared phone rooms for private phone calls based on individual’s use. Figure 9 shows results of motivational programming analysis. Figure 10 shows results of combined spatial and motivational programming analysis. About 65 percent of office space is open space, illustrating transparency and connectivity. About 35 percent of office space is enclosed, showing mostly collaboration and a community that is collective and collective based on office goals. Almost 93 percent of office space is shared, motivating collaboration, integration and connectivity. Furthermore, shared space in this office culture ties to the mobility and the flexibility of an individual’s work preference. Only 7 percent of office space is non-shared. There is a limit to non-shared spaces as it is not important to office work culture.
Spatial and Motivational Programming in Office Environments
HUDDLE PROJECT OFFICE CAFE
LOBBY
STUDIO A OFFICE
M CONF
L CONF LOUNGE
HUDDLE ENTRY
GALLERY A PROJECT CLOS
PHONE
PHONE
COAT
MAIL/COPY
SERVER
STUDIO B
GALLERY B MODEL/PRINT
PROJECT
MATERIAL LIBRARY
SPATIAL PROGRAMMING 0% Enclosed Shared Space: 2,469 sf Enclosed Non-Shared Space: 618 sf Open-Shared Space: 5,613 sf Open Non-Shared Space: 0 sf
28%
RESEARCH FINDINGS
65%
7%
SPATIAL PROGRAMMING TABLE ENCLOSED-SHARED 2,469 28%
PROGRAMS
Conference Room x 5
ENCLOSED-NON-SHARED 618 7% Private Office Space
ENCLOSED-SHARED ENCLOSED-NON-SHARED OPEN-SHARED OPEN-NON SHARED OPEN-NON-SHARED OPEN-SHARED 0 5,613 0% 65%
Kitchen
Phone Room x 3
Material Library
Copy Room
Meeting Tables
Bathrooms
Lounge Workspace
Figure 8: Results of spatial programming for Perkins and Will Minneapolis office.
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HUDDLE PROJECT OFFICE CAFE
LOBBY
STUDIO A OFFICE
M CONF
L CONF LOUNGE
HUDDLE ENTRY
GALLERY A PROJECT CLOS
PHONE
PHONE
COAT
MAIL/COPY
SERVER
STUDIO B
GALLERY B MODEL/PRINT
PROJECT
MATERIAL LIBRARY
MOTIVATIONAL PROGRAMMING Productivity Space: 2,936 sf Engagement Space: 3,739 sf Well-Being Space: 2,621 sf
32%
28%
RESEARCH FINDINGS MOTIVATIONAL PROGRAMMING TABLE
PROGRAMS
PRODUCTIVITY SPACE 2,936 32%
40% PRODUCTIVITY SPACE
ENGAGEMENT SPACE 3,739 40%
WELL-BEING SPACE
WELL-BEING SPACE 2,621 28%
Open Shared Workspace
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Closed-Shared Copy Room
Open-Shared Workspace
Open-Shared Kitchen
Open-Shared Meeting Space
Enclosed-Shared Phone Room
Figure 9: Results of motivational programming for Perkins and Will Minneapolis office.
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ENGAGEMENT SPACE
Spatial and Motivational Programming in Office Environments
Figure 10: Combined results of spatial and motivational programming for Perkins and Will Seattle office.
4.1.4 Tech Office Workspace: Social Media Company The Social Media Company is located in Seattle, and consists of seven floors, but only three floors were researched for this study. Level 1 consists of 20,000 sf, while Level 2 and Level 3 have 16,000 sf and 13,500 sf, respectively. Transparency is an important motivational factor within the office space (clear glazed office space, and clear partitions), and the primary activities are technology-based. In addition to office space, this commercial environment provides additional services to its employees, such as providing three meals a day for a 40 hour work week. Within Level 1, enclosed-shared space makes up 3,076 sf (16 percent) of the office space, including conference rooms, pray rooms, and bathrooms. Enclosed non-shared space is 0 percent. Open shared space makes up 1,780 sf (9 percent) of office space, including lounges, micro-kitchens, and meeting tables. Open non-shared space makes up 14,532 sf (75 percent) of work space, which consists of independent work stations. Overall shared space of Level 1 makes up 25 percent of shared space. Open non-shared space is highly prized on this level, making 75 percent of the entire floor. Figure 11 shows results of spatial programming analysis for Level 1. Within Level 2, enclosed-shared space makes up 3,854 sf (24 percent) of the office space, including conference rooms, pray rooms, mother rooms, computer rooms, laundry rooms, and bathrooms. Enclosed non-
shared space is 0 percent of the total office layout. Open shared space makes up 2,998 sf (19 percent) of the office space. These programs are lounges, microkitchens, and meeting tables. Open non-shared space consists of 9,358 sf (57 percent) of work space. Overall shared space of Level 2 makes up 43 percent of shared space. Open non-shared is highly prized on this level, comprising 57 percent of the floor. Figure 13 shows results of spatial programming analysis for Level 2. Within Level 3, enclosed-shared space makes up 4,316 sf (32 percent) of the office space, including conference rooms, fitness center, pray rooms, mother rooms, computer rooms, well-being room and bathrooms. Enclosed non-shared space is 0 percent. Open shared space makes up 2,071 sf (15 percent) of office space, including open lounges, micro-kitchens, meeting tables, and decks. Open-non shared space makes up 7,173 sf (53 percent) of workspace. Overall shared space of Level 3 makes up almost half 47 percent of shared space. Figure 15 shows results of spatial programming analysis for Level 3. Regarding motivational programming analysis, on Level 1 there are 14,532 sf (77 percent) of space in the office used for productivity. This relates to the work culture form of spatial transparency. Secondly, there are 2,546 sf (14 percent) of office space that is considered engagement space; program includes open-shared meeting table, and enclosed shared conference rooms. These spaces allow the work culture to reinforce con-
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nectivity and interaction within teams. Finally, there are 1,705 sf (9 percent) office space dedicated to well-being. These programs include bathrooms, micro kitchens, open lounges, and enclosed pray rooms. Typically, other office workspaces usually provide three spaces dedicated to well-being, such as lounges, bathrooms, and break rooms whereas this work culture is able to provide more spaces. These programs include bathrooms, micro kitchens, open lounge, and enclosed pray rooms, computer rooms, laundry room, and mother’s room. Figure 12 shows results of motivational programming analysis for Level 1.
at (35 percent). Engagement is relatively close, making 30 percent. Figure 16 shows results of motivational programming analysis for Level 2.
On Level 2, there are 3,757 sf (36 percent) of space in the office used for productivity. Second, there are 2,014 sf (19 percent) of office space that is considered engagement space; program includes open-shared meeting tables, and enclosed share conference rooms. Lastly, there are 4,608 sf (45 percent) of office space dedicated to well-being. These programs include bathrooms, micro kitchens, open lounge, and enclosed pray rooms, computer rooms, laundry room, and mother’s room. Figure 14 shows results of motivational programming analysis for Level 2.
Figure 18 shows combined results for Level 2, where 76 percent of office space is open space illustrating a lot of transparency and connectivity. 24 percent of office space is enclosed, showing limited collaboration. 43 percent of office space is shared, motivating collaboration, integration and connectivity goals. 57 percent of office space is non-shared. Non-shared indicates that productivity is most important in this office space.
On Level 3, there are 3,193 sf (35 percent) of space in the office used for productivity. Secondly, there are 2,800 sf (30 percent) of office space for engagement space; program includes enclosed-shared conference rooms, and open-shared meeting space. Lastly, there are 3,194 sf (35 percent) of office space dedicated to well-being. These programs include open shared lounges, open shared kitchens, enclosed shared pray rooms, enclosed shared bathrooms, enclosed shared fitness centers, enclosed shared well-being rooms, and enclosed shared mother rooms. Level 3 prioritizes productivity and well-being around the same percentage, each
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Figure 17 shows combined results for Level 1, where 84 percent of office space is open space illustrating transparency and connectivity. 16 percent of office space is enclosed. 25 percent of office space is shared, motivating collaboration, integration and connectivity goals. 75 percent of office space is non-shared. Productivity is most important, then engagement and well-being is the last on this floor.
Figure 19 shows combined results for Level 3, where 68 percent of office space is open space illustrating transparency and connectivity. 32 percent of office space is enclosed, while 47 percent of office space is shared, motivating collaboration, integration and connectivity goals. 53 percent of office space is non-shared. Nonshared means work productivity is most important in this office space. In terms of motivational factors, there is range from productivity being the highest on Level 1. Then, well-being slowly overpowers within a good percentage of the floor area, defining more of a balance in their work culture.
Spatial and Motivational Programming in Office Environments
LEVEL 01 1
SPATIAL PROGRAMMING 16%
0%
Enclosed Shared Space: 3,076 sf Enclosed Non-Shared Space: 0 sf Open-Shared Space: 1,780 sf Open Non-Shared Space: 14,532 sf
9%
RESEARCH FINDINGS SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 3,076 16%
ENCLOSED-NON-SHARED 0 0%
75%
ENCLOSED-SHARED
OPEN-SHARED 1,780 9%
Conference Room x 13
Lounge
Pray Room
Micro-Kitchen
Bathrooms
Meeting Tables
ENCLOSED-NON-SHARED
OPEN-SHARED
OPEN-NON SHARED
OPEN-NON-SHARED 14,532 75% Workspace
Figure 11: Results of spatial programming for for Social Medial Company, Level 1.
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LEVEL 01 1
MOTIVATIONAL PROGRAMMING 9%
Productivity Space: 14,532 sf Engagement Space: 2,546 sf Well-Being Space: 1,705 sf
14%
RESEARCH FINDINGS
77%
MOTIVATIONAL PROGRAMMING TABLE PRODUCTIVITY SPACE 14,532 77%
PROGRAMS
Open Non-Shared Workspace
PRODUCTIVITY SPACE
ENGAGEMENT SPACE 2,546 14%
WELL-BEING SPACE
WELL-BEING SPACE 1,705 9%
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Open-Shared Meeting Space
Open-Shared Kitchen
Figure 12: Results of motivational programming for Social Medial Company, Level 1.
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ENGAGEMENT SPACE
Enclosed-Shared Pray Room
Spatial and Motivational Programming in Office Environments
LEVEL 2
SPATIAL PROGRAMMING 24%
Enclosed Shared Space: 3,854 sf Enclosed Non-Shared Space: 0 sf Open-Shared Space: 2,998 sf Open Non-Shared Space: 9,358 sf
0% 57% 19%
RESEARCH FINDINGS SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 3,854 24%
ENCLOSED-NON-SHARED 0 0%
ENCLOSED-SHARED
OPEN-SHARED 2,998 19%
Conference Room x 20
Lounge
Pray Room
Micro-Kitchen
Bathrooms
Meeting Tables
ENCLOSED-NON-SHARED
OPEN-SHARED
OPEN-NON-SHARED 9,358 57%
OPEN-NON SHARED
Workspace
Mothers Room My Documents Monkey Diswasher
Figure 13: Results of spatial programming for for Social Medial Company, Level 2.
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LEVEL 2
MOTIVATIONAL PROGRAMMING Productivity Space: 3,757 sf Engagement Space: 2,014 sf Well-Being Space: 4,608 sf
36% 45%
RESEARCH FINDINGS MOTIVATIONAL PROGRAMMING TABLE PRODUCTIVITY SPACE 3,757 36%
PROGRAMS
Open Non-Shared Workspace
19% PRODUCTIVITY SPACE
ENGAGEMENT SPACE 2,014 19%
ENGAGEMENT SPACE
WELL-BEING SPACE
WELL-BEING SPACE 4,608 46%
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Open-Shared Meeting Space
Open-Shared Kitchen Enclosed-Shared Pray Room Enclosed-Shared Mothers Room Enclosed-Shared Laundry Room Enclosed-Shared My Documents Room
Figure 14: Results of motivational programming for Social Medial Company, Level 2.
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Spatial and Motivational Programming in Office Environments
LEVEL 3
SPATIAL PROGRAMMING Enclosed Shared Space: 4,316 sf Enclosed Non-Shared Space: 0 sf Open-Shared Space: 2,071 sf Open Non-Shared Space: 7,173 sf
32% 53% 0%
RESEARCH FINDINGS
15%
SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 4,316 32%
ENCLOSED-NON-SHARED 0 0%
ENCLOSED-SHARED
OPEN-SHARED 2,071 15%
Conference Room x 17
Open Lounge
Fitness Center
Micro-Kitchen
Pray Room
Meeting Tables
Bathrooms
Deck
ENCLOSED-NON-SHARED
OPEN-SHARED
OPEN-NON-SHARED 7,173 53%
OPEN-NON SHARED
Workspace
Mothers Room My Documents Well-Being Room
Figure 15: Results of spatial programming for for Social Medial Company, Level 3.
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LEVEL 3
MOTIVATIONAL PROGRAMMING Productivity Space: 3,193sf Engagement Space: 2,800 sf Well-Being Space: 3,194 sf
35%
35%
RESEARCH FINDINGS
30%
MOTIVATIONAL PROGRAMMING TABLE PRODUCTIVITY SPACE 3,193 35%
PROGRAMS
Open Non-Shared Workspace
PRODUCTIVITY SPACE
ENGAGEMENT SPACE 2,800 30%
ENGAGEMENT SPACE
WELL-BEING SPACE
WELL-BEING SPACE 3,194 35%
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Open-Shared Meeting Space
Open-Shared Kitchen Enclosed-Shared Pray Room Enclosed-Shared Bathroom Enclosed-Shared Fitness Center Enclosed-Shared Well-Being Room Enclosed-Shared Mothers Room
Figure 16: Results of motivational programming for Social Medial Company, Level 3.
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Spatial and Motivational Programming in Office Environments
LEVEL 1
Figure 17: Combined results of spatial and motivational programming for Social Media Company, Level 1. LEVEL 2
Figure 18: Combined results of spatial and motivational programming for Social Media Company, Level 2. LEVEL 3
Figure 19: Combined results of spatial and motivational programming for Social Media Company, Level 3.
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4.1.5 Research Workspace: Research & Healthcare Facility This case study is a higher education and science technology research office, combined with healthcare (Figure 20). Patient care and research are the focus of this institution. Patient privacy, patient care, and well-being were important considerations for the design of this case study. In this research, two floors (Level 2, consisting of 11,400 sf and Level 4, consisting of 52,000 sf) were evaluated. As seen in Figure 20, enclosed-shared space makes up 5,686 sf (50 percent) of the office space for Level 2. The program includes conference rooms, printing room, bathrooms, urgent care teaming areas, consultation rooms, and medical rooms. Enclosed non-shared space makes up 618 sf (5 percent) of the office. Open shared space makes up 3,852 sf (34 percent) of office space, including break rooms, open lounges, waiting areas, reception, and team meeting areas. Open-non shared space constitutes 1,217 sf (11 percent) of work space, including staff work stations and open office. Overall shared space for Level 2 typical clinic plan makes up 84 percent of shared space. Non-shared spaces are only 16 percent of office space. As seen in Figure 22, enclosed-shared space makes up 2,123 sf (15 percent) of the office space for Level 4. The program includes conference rooms, bathrooms, break room, and lab rooms. Enclosed non-shared space makes up 4,052 sf (30 percent) of the office, including private offices, and lab rooms. Open shared space makes up 5,436 sf (40 percent) of office space. These programs are open labs and team lounge meetings. Open-non shared space constitutes 2,059 sf (15 percent) of work space. Open non-shared space makes up 2,059 sf (15 percent) of office space (open office).
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Overall shared space for Level 4 typical clinic plan makes up 55 percent of shared space. Non-shared spaces are only 45 percent of office space. Motivational programming analysis results for Level 2 are shown in Figure 21. There are 3,064 sf (39 percent) of space in the office that is used for productivity (open non-shared workspace and open non-shared open office). There are 749 sf (9 percent) of office space that is considered engagement space; the program includes enclosed-shared conference rooms, enclosed shared teaming area, and enclosed shared consult room and open-shared reception space. These elements qualify as engagement spaces due to connectivity between patient care and medical doctors. Lastly, there is a 4,110 sf (52 percent) office space dedicated to well-being, including open shared lounge for independent time or collaborative spaces, open shared break room for dining with flexibility in its use based on the individual’s choice and or in an open collaborative manner, enclosed shared exam rooms for patient care and privacy, and an enclosed-shared bathrooms. Motivational programming analysis results for Level 2 are shown in Figure 23. There are 6,975 sf (52 percent) of space that is used for productivity, including open non-shared private offices, open non-shared open office, and enclosed shared lab rooms. There are 5,435 sf (41 percent) of office space that is considered engagement space; including enclosed-shared team lounges, meetings rooms, and open lab. These elements qualify as engagement spaces due to connectivity between research labs and researchers. Lastly, there are 905 sf (7 percent) office space dedicated to well-being, including open shared lounges for independent time or collaborative spaces, open shared break room for dining, and an enclosed-shared bathrooms.
Spatial and Motivational Programming in Office Environments
LEVEL 2
SPATIAL PROGRAMMING Enclosed Shared Space: 5,686 sf Enclosed Non-Shared Space: 618 sf Open-Shared Space: 3,852 sf Open Non-Shared Space: 1,217 sf
50%
34%
RESEARCH FINDINGS SPATIAL PROGRAMMING TABLE ENCLOSED-SHARED 5,686 50%
PROGRAMS
Conference Room x 1
ENCLOSED-NON-SHARED 618 5% Private Office
11%
ENCLOSED-SHARED
5% ENCLOSED-NON-SHARED
OPEN-SHARED 3,852 34%
OPEN-SHARED
OPEN-NON SHARED
OPEN-NON-SHARED 1,217 11%
Break Room
Staff Work Station
Printing Room
Open Lounge
Open Office
Bathrooms
Waiting Area
Toilet x 2
Reception
Urgent Care Teaming Area
Team Meeting Area
Copy Room Consult Room Med Room
Figure 20: Results of spatial programming for a research & healthcare facility, Level 2.
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LEVEL 2
MOTIVATIONAL PROGRAMMING Productivity Space: 3,064 sf Engagement Space: 749 sf Well-Being Space: 4,110 sf
39%
52%
RESEARCH FINDINGS 9%
MOTIVATIONAL PROGRAMMING TABLE
PROGRAMS
PRODUCTIVITY SPACE 3,064 39%
PRODUCTIVITY SPACE
ENGAGEMENT SPACE 749 9%
WELL-BEING SPACE
WELL-BEING SPACE 4,110 52%
Open Non-Shared Work Station
Enclosed-Shared Conference Rooms
Open-Shared Lounge
Open Non-Shared Open Office
Enclosed-Shared Training Area
Open-Shared Break Room
Enclosed-Shared Consult Room
Enclosed-Shared Phone Room
Open-Shared Reception Space
Enclosed-Shared Bathroom Enclosed-Shared Exam Room
Figure 21: Results of motivational programming for a research & healthcare facility, Level 2.
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ENGAGEMENT SPACE
Spatial and Motivational Programming in Office Environments
LEVEL 4
SPATIAL PROGRAMMING 15%
Enclosed Shared Space: 2,123 sf Enclosed Non-Shared Space: 4,052 sf Open-Shared Space: 5,436 sf Open Non-Shared Space: 2,059 sf
15%
30%
40%
RESEARCH FINDINGS SPATIAL PROGRAMMING TABLE
PROGRAMS
ENCLOSED-SHARED 2,123 15%
ENCLOSED-NON-SHARED 4,052 30%
ENCLOSED-SHARED
ENCLOSED-NON-SHARED
OPEN-SHARED 5,436 40%
Conference Room x 1
Private Office
Open Lab
Bathroom
Lab Rooms
Team Lounge Meetings
OPEN-SHARED
OPEN-NON SHARED
OPEN-NON-SHARED 2,059 15%
Office Space
Break Room Lab Rooms
Figure 22: Results of spatial programming for a research & healthcare facility, Level 4.
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LEVEL 4
MOTIVATIONAL PROGRAMMING 7%
Productivity Space: 6,975 sf Engagement Space: 5,435 sf Well-Being Space: 905 sf
52% 41%
RESEARCH FINDINGS
PROGRAMS
MOTIVATIONAL PROGRAMMING TABLE PRODUCTIVITY SPACE
Open Non-Shared Open Office
Enclosed-Shared Team Lounge
Open-Shared Lounge
Enclosed Private Office
Open Lab
Open-Shared Break Room
Enclosed Shared lab Rooms
WELL-BEING SPACE
WELL-BEING SPACE 905 7%
ENGAGEMENT SPACE 5,435 41%
Enclosed-Shared Bath Room
Figure 23: Results of motivational programming for a research & healthcare facility, Level 4.
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ENGAGEMENT SPACE
PRODUCTIVITY SPACE 6.975 52%
Spatial and Motivational Programming in Office Environments
LEVEL 2
Figure 24: Combined results for a research & healthcare facility, Level 2.
LEVEL 4
Figure 25: Combined results for a research & healthcare facility, Level 4.
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5.0 SUMMARY 5.1 Spatial Programming
Within each industry type, spatial progamming provides a general guideline to determine enclosed shared, enclosed non-shared, open-shared, and open non-shared spaces. Figure 26, summarizes a comparison of these different industry spaces to co-working space. Enclosed-Shared Spaces: Results suggest that enclosed shared spaces for co-working office constitute 12 percent compared to other industry types (within 25 to 30 percent of the overall space). Enclosed Non-Shared Spaces: Results suggest enclosed non-shared spaces constitute 63 percent of coworking office environment compared to other industry types (research and design offices) that stay within the range of 4 to 18 percent.
Open-Shared Spaces: Result suggest that co-working open-shared spaces is 26 percent compared to other industry types that stays within the range of 14 to 46 percent of their overall space. Open Non-Shared Spaces: Results suggest that coworking open non-shared spaces is 0 percent compared to other industry types that stays within the range of 13 to 62 percent. Overall, co-working office environment highly prioritizes enclosed non-shared spaces and has a higher percetage compared to other industry types. These programs include private office spaces, which accommodate small sized, 1 person office space to large enterprises of 30 to 50 people office space. In addition, open shared spaces becomes second in spatial planning. These programs include common spaces; open lounges, kitchen, and shared hot desks.
TECH
RESEARCH Chart Title
DESIGN Chart Title
13%
Spatial
24% 33% 26% ENCLOSED SHARED
ENCLOSED SHARED
ENCLOSED NON-SHARED
ENCLOSED NON-SHARED
OPEN SHARED
OPEN SHARED
14%
OPEN NON-SHARED
25%
ENCLOSED SHARED
4%
ENCLOSED NON-SHARED
OPEN NON-SHARED
OPEN SHARED OPEN NON-SHARED
36% 18%
Spatial
62%
46%
Chart Title
Chart Title
33%
CO-WORKING
13%
0%,
24% 25%
12%, 26%,
12%
26%
RESEARCH
26%
TECH
ENCLOSED SHARED 4%
ENCLOSED NON-SHARED
14%
OPEN SHARED
36%
18% 46%
62%
63%,
ENCLOSED NON-SHARED
DESIGN
OPEN NON-SHARED
OPEN SHARED
OPEN NON-SHARED ENCLOSED SHARED ENCLOSED NON-SHARED
62%
OPEN SHARED OPEN NON-SHARED
Figure 26: Spatial programming average comparison results between different industries (Office, Tech, & Research).
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ENCLOSED SHARED
Spatial and Motivational Programming in Office Environments
5.1 Motivational Programming
Within each industry type, motivational progamming provides a general guideline to determine spaces that promote productivity, engagement, and well-being. Figure 27 summarizes a comparison of these different industry spaces to the co-working office environment. Productivity: Results suggest that productivity spaces within co-working environment constitutes 72 percent compared to other industry (43 to 45 percent of overall space).
Well-Being: Results suggest spaces dedicated to wellbeing constitute 22 percent of co-working office environment, compared to other industry types (23 to 30 percent). Overall, co-working offices highly prioritize productivity. These programs include private office spaces within an enclosed non-shared spaces. Well-being becomes second in motivational planning. These programs make up common spaces and are “shared� through open and enclosed space: open lounges, kitchen, phone rooms, bathrooms, and shared hot desks.
Engagement: Results suggest that engagement spaces within co-working environment make up 6 percent, compared to other industry types (21 to 34 percent of the overall space).
RESEARCH Chart Title
TECHChart Title
45%
DESIGN Motivational
49%
43%
23%
30%
30%
PRODUCTIVITY
PRODUCTIVITY
PRODUCTIVITY
ENGAGEMENT
ENGAGEMENT
ENGAGEMENT
WELL-BEING
WELL-BEING
WELL-BEING
25%
Motivational
21%
Chart Title
34%
Chart Title
CO-WORKING
45% 49% 43%
22% 22%,
RESEARCH 23%
PRODUCTIVITY
PRODUCTIVITY
6% 6%,
ENGAGEMENT TECH
ENGAGEMENT
WELL-BEING
30% 30% WELL-BEING
DESIGN 72%, 72%
21% 34% 25%
PRODUCTIVITY ENGAGEMENT WELL-BEING
Figure 27: Motivational programming average comparison results between different industries (Office, Tech, & Research).
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6.0 CONCLUSION
As workplace design gets more complex, and progresses to more co-shared working spaces, the next steps for this research would be to include a more diverse group of case studies, which require very different space requirements. However, by evaluating these case studies through the lenses of spatial and motivational programming, the collected data suggests that some industries benefit from using a traditional enclosed workspace while others benefit from a more flexible open-shared workspace. In comparing the case studies, the study concluded that in addition to the critical and necessary open and enclosed environments, new trends require spaces for increased productivity, engagement and well-being. Technology-enabled and socially rich work environments have given the impetus to transition most work environments to becoming more global in scale. In the future with a more global influence, these shifts in workplace design are going to be constantly evolving. The results indicate that the design offices, favor an open office environment, as this arrangement allows for more transparency within the work environment. The technology offices favor open non-shared offices since this layout promotes collaboration. In the research offices, enclosed shared workspaces are prevalent, which support administration, patient care, and lab work. The next step for this research would be to create a tool that supports generic spatial or motivational programming based on industry types, as it would demonstrate more eloquently the value behind this methodology.
Acknowledgments
This study sprouted from an Innovation Incubator Grant from the Perkins and Will Seattle Office, and was conducted with teammates; Wei Tang and Simon Chavez. Wei Tang assisted in data collection and graphic representation, and Simon Chavez assisted with the background research. Janice Barnes provided initial insight and guidence for this research. The author would also like to acknowledge Gavin Smith, Carsten Stinn and Christa Woods, who guided this research.
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REFERENCES
[1] Duffy, F., (1997). The New Office, London, UK: Conran Octopus. [2] Gensler, (2012). “What We’ve Learned about Focus in the Workplace”, Report, Retrieved on 10/2017 from https://www.gensler.com/uploads/documents/Focus_in_the_Workplace_10_01_2012.pdf. [3] Spira, J., and Feintuch, J., (2005). “The Cost of Not Paying Attention: How Interruptions Impact Knowledge Worker Productivity”, Report, Retrieved on 10/2017 from http://www.brigidschulte.com/wp-content/uploads/2014/02/costofnotpayingattention.basexreport-2.pdf. [4] Gabor, N., O’Neill, M., Johnson, B., and Bahr, M., (2016). “Designing for Focus Work”, Report, Retrieved on 10/2017 from http://eu.haworth.com/docs/ default-source/white-papers/designing-for-focus-work. pdf?sfvrsn=6. [5] Barnes, J., Adler, N., Wineman, J., Peponis, J., and Graham, L., (2017). “UCSF School of Medicine Workplace Research Study Mission Hall”, Report, Retrieved on 10/2017 from https://space.ucsf.edu/sites/ space.ucsf.edu/files/wysiwyg/UCSF-SOM_WRS%20 Final%20Report__032717.pdf. [6] U.S. Census Bureau, (2015). “Millennials Outnumber Baby Boomers and are Far More Diverse”, Report, Retrieved on 10/2017 from https://www.census.gov/ newsroom/press-releases/2015/cb15-113.html. [7] Gensler, (2013). “2013 U.S. Workplace Survey: Key Findings,” Report, Retrieved on 10/2017 from https:// www.gensler.com/uploads/document/337/file/2013_ US_Workplace_Survey_07_15_2013.pdf.
PEER REVIEWERS Dr. Jason Brown Georgia Institute of Technology
Dr. Souroush Farzinmoghadam Worcester Polytechnic Institute
Clifton Fordham Temple University
Dr. Ying Hua Cornell University
Dr. Jihun Kim New York City College of Technology
Dr. Thanos Tzempelikos Purdue University
Dr. Meredith Wells Lepley University of Southern California
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AUTHORS 01.
CHENEY CHEN Cheney is a Senior Sustainable Building and Energy Engineer with Perkins and Will Vancouver office. Drawing upon his background in architecture and building science, Cheney excels at communicating effectively across the disciplines to optimize project success.
02.
AJLA AKSAMIJA Ajla is an Associate Professor at the University of Massachusetts Amherst and Building Technology Researcher/Associate at Perkins and Will. Her research expertise includes building science and sustainability, emerging technologies, highperformance buildings, digital design and representations. She has contributed chapters for many books, and published over seventy research articles. She is the author of two books, Sustainable Facades: Design Methods for High-Performance Building Envelopes (2013), and Integrating Innovation in Architecture: Design, Methods and Technology for Progressive Practice and Research (2016).
02.
DYLAN BROWN Dylan is a project designer at Lavallee Brensinger Architects, and a recent graduate from the graduate architecture program at the University of Massachusetts, Amherst. Dylan works on residential, education, and science/laboratory projects. His research area includes building simulations and modeling, digital design and new types of facade systems.
03.
TINA NGUYEN Tina is an architectural designer in Perkins and Will’s Seattle office. She has worked on a variety of project types, including residential high-rise buildings, healthcare, laboratory, commercial offices, and interior design projects. Tina’s research interests focus on user’s experience and work-shared spaces, relating to well-being, engagement and productivity.
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