Building Performance Modeling_Spring 2023

BUILDING PERFORMNCE MODELING

TCS Hall

Spring 2023

Yihan Ma

Jiahua Wu

Chuyi Wang

CHAPTER I. SIMPLE BOX MODELING/ITERATIVE DESIGN

1. Climate Analysis

In Rhino-LadyBug and Climate consultant, we extracted weather data from the Pittsburgh International Airport and generated graphs of weather components.

1.1 Pittsburgh Climate

Puttsburgh, time zone -5, is located at 40.5 °N with a longitude of -80.23. According to the IECC 2021 climate zone map, Pittsgurgh is located in climate zone 5A, which represents for cool and humid climate.

We can indicate that there is a huge difference on the amounts of HDD and CDD for 10°C with CDD a lot higher makes Pittsburgh have max frequency of temperature ranges between 10°C and 18°C.

The dry-bulb temperature (DBT) is the temperature of air measured by a thermometer freely exposed to the air, but shielded from radiation and moisture. Minimum dry-bulb temperature in Pittsgurgh is18.9 °C while the maximum could reach 32.8 °C, which means Pittsburgh has relatively mild summer and extremely cold winter.

Relative humidity(RH) is the factor for indicating moisture levels. Pittsburgh experiences moderate humidity levels throughout the year, with an average relative humidity of 68%. Relative humidity in Pittsburgh could be as low as 18% and also can be as high as 100% while the average relative humidity is 68%. The humidity tends to be highest during the summer months, which can make the weather feel more uncomfortable.

Pittsburgh experiences a range of wind speeds and directions, depending on the season and weather patterns. The average wind speed in Pittsburgh is around 3-5 m/s (7-11 mph), with occasional gusts up to 15 m/s (34 mph) during storms.

Ground temperature is the measure of how hot or cold the gound is. The ground temperatures in Pittsburgh tend to be relatively cool, with an average temperature of around 12°C (54°F) throughout the year. The temperature ranges from -2°C to 23°C at 0.5 meters depth, 3°C to 18°C at 2 meters and 5°C to 16°C at 4 meters. This indeicates that lower depth is, wider the ground temperature range is.

Insolation is the amount of solar radiation reaching a given area. Pittsburgh receives moderate amounts of sunshine throughout the year, with an average of around 160-180 days of sunshine per year. The average solar radiation density, or insolation, is around 100-150 W/m2 during the summer months, and around 50-100 W/m2 during the winter.

Cloud cover refers to the fraction of the sky obscured by clouds on average when observed from a particular location. Pittsburgh has a relatively high cloud cover, It can reach 100% in certain months like December, or as low as 12% in July, with an average cloud fraction of around 60% throughout the year. Cloud cover tends to be highest during the winter months, which can help to moderate the temperatures to some extent.

1.2 Miami Climate

Miami, FL, is located in the 1A climate zone according to the IECC 2021 climate map, which means that it has a very hot humid weather throughout the year.

As plotted in the graph, HDD in Miami at 10°C is 1 and CDD at 10°C is 5447 while HDD at 18°C is 72 and the CDD at 18°C is 2477.

The dry-bulb temperature varies from 15.5°C to 35°C throughout the year in Miami. On average, the temperature is 24°C, with the highest temperatures measured in July (about 35°C). The coldest month of the year in Miami is January, but the lowest temperatures were recorded in February, at 5°C.Thus, the weather also significantly depends seasonally.

The humidity is above 60% from May to December. The relative humidity decreases to some extent during winter and can reach 24% least while in summer could be as high as 100%. Overall, Miami is still a humid place with oppressive conditions.

Over the course of the year, Miami’s major hourly average wind direction is east. Ranging from 1.5m/s to 12.9m/s, the average wind speed in Miami is around 4-5 m/s, during the summer(May to October), the wind is calmer. It’s mainly because of the sea breeze, which blows from the east during the day.

Annual

The average ground temperature is about 25°C throughout the year. The trend of temperature change in the shallow soil (0.5) is approximately the same as the temperature, and there is a lag in the deep soil(4.0m).

Miami is known for its abundant sunshine, with an average of around 300 days of sunshine per year. The highest average Solar Radiation Density month is April, which can be 450W/m2, followed by July. The annual hourly average is around 400 W/m2.

Miami‘s cloud fraction is above the US average, at around 55%. Cloud cover tends to be highest during the summer months, from June to September, helping to moderate temperatures.

2. Baseline Model

To begin with, we created a baseline model as shown in Figure 1.2.1, which is a rectangular-shape building, in IESVE for Pittsburgh and Miami.

The energy consumption breakdown for both cities are as follows:

As Pittsburgh is located in cool and humid climate zone, which has a high demand for heating in winter months. Thus, the nuatural gas consumption is great. In comparison to the climate difference, Miami is located in hot humid climate zone that requires a lot of cooling with almost no heating is needed.

Though Pittsburgh has more gas consumption than Miami, the total energy consumption is still lower than

Figure 1.2.1 Baseline model shape

Pittsburgh’s baseline model Energy consumption breakdown.

Miami’s baseline model Energy consumption breakdown.

3. Building Shape Comparison

Building shape coefficient has a great impact on its energy load since the exposure surface area varies with different shapes.

In this section, we are going to alternate the baseline model to five different shapes of the building and see which building shape will result in lower EUI, hence, better energy effieciency. According to single variable principle, all other factors. including floor area, building height, window-wall ratio, and components, are kept the same. The following is the specific information of five selected comparison samples in the order of surface area from the smallest to largest.

Form 1 H-shape

External surface area: 6125.68m2

shape coefficient: 31.04%

This shape has the most complicated geometry and the largest exposure surface area, which would be the best explanation of the effect of form complexity on buidling performance.

Form 2 L-shape

External surface area: 5576m2

shape coefficient: 29.34%

This shape is selected to test whether a strip volume with two longer wings would have a better energy performance.

Form3 T-shape

External surface area: 5696m2

shape coefficient: 28.88%

Compared with the L-shape, the T-shaped building has a more concentrated volume and shorter wings but larger surface area.

Form 4 U-shape

External surface area: 6125.68m2

shape coefficient: 31.04%

Compared with the baseline model, a courtyard is extracted on the north, so it is longer on each side in order to keep the GFA consistent. This shape is utilized to explore the influence of a backyard on energy consumption.

Form 5 Rectangle Shape (Baseline)

External surface area: 5257.57m2

shape coefficient: 26.65%

The baseline model is a three-storages building which gross floor area is 1660.56 m2. The total building height is 11.88m, with each floor at 3.96m.

3.1 Pittsburgh Building Shape Comparison

Total Energy: 379.82 MWh

EUI: 76.24 kWh/m^3

Total Energy: 382.75 MWh

EUI: 76.63 kWh/m^3

Total Energy: 377.593 MWh

EUI: 75.79 kWh/m^3

Total Energy: 383.22MWh

EUI: 76.93 kWh/m^3

Total Energy: 368.19 MWh

EUI: 73.91 kWh/m^3

For Pittsburgh, based on all results of the five building shapes, rectangular is the most energy efficient one, whereas the H-shaped consumes most and U-shaped does not lag far behind. When we observe the results in relation to the unique variables of the five samples, it is clear that the shape coefficient has an effect on energy consumption. The baseline model, which is a rectangluar shape, maintains the lowest EUI while all building components are identical. We can indicate that of all the five forms, the larger the surface area, the higher the energy consumption.

3.2 Miami Building Shape Comparison

Total Energy: 387.93 MWh

EUI: 77.87 kWh/m^3

Total Energy: 387.93 MWh

EUI: 77.87 kWh/m^3

Total Energy: 387.93 MWh

EUI: 77.87 kWh/m^3

Total Energy: 387.93 MWh

EUI: 77.87 kWh/m^3

Total Energy: 368.19 MWh

EUI: 73.91 kWh/m^3

For Miami, the total energy consumption ranges from 368 MWh to 383 MWh, while the EUI for different fothe building shape that results in the lowest EUI is also the baseline model while all other building components are identical. Likewise, the baseline model consumes the least amount of energy, while the U-shaped building consumes the most. However, Miami's results do not exactly follow the principle that the building shape factor is positively related to the total energy consumption. The total energy consumption of H-shaped builiding is lower than the L-shaped one, while the H-shaped building's surface-to-volume ratio is larger than L-shaped.

3.3 Building Shape Comparison Summary

Surface area has a great impact on the building loads and energy consumption. The building form with a small external wall area has a small thermal conductivity area, so the energy consumption is correspondingly low. The reason for the slight difference between the two results is that gas consumption dominates in cold regions, while electricity consumption plays a decisive role in tropical regions.

4. Building Component Iteration and Comparison

In this section, we are going to iterate building components, including the window-to-wall ratio(WWR), wall component, roof, slab, and window glazing, based on the building shape we selected as lowest EUI for Pittsburgh and Miami in the previous section.

4.1 Window-to-wall Ratio (WWR)

The Window-to-Wall Ratio (WWR) is the fraction of the above grade wall area that is covered by fenestration, calculated as the ratio of the wall fenestration area to the gross above grade wall area.

As is shown in the figure above, based on the best-case model from the building from simulation, of all the five WWR parameter, the energy consumption ranges from 305 MWh to 318 MWh. 20% is the most energy efficient one, which EUI is approximately 68.37 kWh/m2, whereas the 60% consumes most, which consumes roughly 76.09 kWh of energy per square meter. At the same time, 20%-WWR building also has the lowest carbon emission, which is 221492kg.

The results for Miami are similar. The energy consumption ranges from 354 MWh to 382 MWh. The 20%-WWR model consumes the least amount of energy, while the 60%-WWR model consumes the most. The EUI for the best-case model is 71.10, and the carbon emission is 213251kg.

When we observe the results in relation to the WWR parameters of the five samples, it is clear that the WWR has an great effect on energy consumption. That is to say, the larger the glazing area, the higher the energy consumption. In cold region, windows cause a lot of infiltration and thermal conduction during the winter. The larger the area is, the larger the heat loss could be. So it requires more energy to heat the indoor space. On the contrast, in hot region, windows can bring a lot of solar radiation, which results in

Table 4.1.1 Pittsburgh - Baseline - 20% WWR

Table 4.1.2 Miami - Baseline - 20% WWR

4.2 Wall Components

Wall components are the physical structures of the components of a wall, such as concrete, CMU, studs, GWB, insulation, and air gaps.

As is shown in the figure above, based on the best-case model from the WWR simulation, of all the eight different wall assemblies, the energy consumption ranges from 340 MWh to 835 MWh. the baseline model(2013 external wall) is the most energy efficient one, which EUI is approximately 68.37 kWh/m2, whereas the SHTS0000(Uvalue=6.679) consumes most, which is roughly 167.65 kWh of energy per square meter. At the same time, the baseline model also has the lowest carbon emission, which is 221492kg. According to IECC 2021, the maximum U-factor of steel framed external wall in climate 5A is 0.064.

The results for Miami are similar. The energy consumption ranges from 354 MWh to 389 MWh. The 2013 external model (U-value=0.261), consumes the least amount of energy, while the SHTS0000(U-value=6.679) model consumes the most. The EUI for the bestcase model is 71.10, and the carbon emission is 213251kg. According to IECC 2021, the

As is shown in the figure above, the U-value of the external wall plays an important role in energy consumption. Since U-value represents the thermal conductivity ability of the component, the higher the U-value is, the larger the heat transfers. In cold region, with a larger U-value, the building heat loss in winter is high, so it requires more energy load to ensure the indoor temperature could reach the comfortable zone. Whereas, in cold region, the heat gain of a building with a larger U-value external wall in summer is hign,

4.3 Roof

Roof is the structure forming the upper covering of a building.

Seven alternative roof assembly construction options were explored using the bestcase model from the wall construction simulations (Form baseline-Rectangle with 20% WWR and a frame wall construction 2013 External Wall). According to IECC 2021, the maximum U-value of a nonresidential structure with insulation totally above deck roof standards for climatic zone 5A is 0.181(W/m^2`K). According to the iterations result, the baseline 2013 Roof, with the lowest U-value(0.180 W/m^2`K) has the lowest energy consumption (EUI 68.37 kWh/sqm/year) and carbon emission(221492 kg). Improved insulation is expected to improve with EUI.

Table 4.2.1 Pittsburgh - Baseline - U-VALUE: 0.261

Table 4.2.2 Miami - Baseline - U-VALUE: 0.261

Seven alternative roof assembly construction options were explored using the bestcase model from the wall construction simulations (Form baseline-Rectangle with 20% WWR and a frame wall construction 2013 External Wall). According to ASHRAE 90.1, the maximum U-value of a nonresidential structure with insulation totally above deck roof standards for climatic zone 1A, is 0.360(W/㎡K). According to the iterations result, the baseline 2013 Roof, with the lowest U-value(0.180 W/㎡K) has the lowest energy

The U-value of a roof can significantly affect the building loads and energy consumption, a lower U-value indicates that the roof has a better insulating performance. In cool climates, an uninsulated roof with a high U-value can result in high heating loads, requiring more energy to heat the structure and increasing running expenses. In hot climates, a poorly insulated roof can result in high cooling loads, which means that more energy is required to cool the building. Roof construction options are pretty much entirely limited to material selections based on thickness, density, conductivity, and specific heat capacity. Pittsburgh’s CO2 emissions are higher than Miami’s, primarily due

4.4 Slab

A slab refers to a floor that has been formed using concrete (and generally steel reinforcement) and may form part of the structure of a building. It may form the floor of a basement, at ground level or at upper levels.

Six alternative slab assembly construction options were explored using the best-case model from the roof construction simulations (Form baseline Rectangle with 20% WWR, a frame wall construction 2013 External Wall, and 2013 Roof). According to IECC 2021, the maximum F-factor of nonresidential structure for heated slabs for climatic zone 5A is 0.62.

According to the iterations result, the 2013 Exposed Floor (U-value 0.256 W/㎡K, unknown F-factor), has the lowest energy consumption (EUI 68.37 kWh/sqm/year) and carbon emission(221492 kg).

Table 4.3.1 Pittsburgh - Baseline - U-VALUE: 0.180

Table 4.3.2 Miami - Baseline - U-VALUE: 0.180

A poorly insulated slab with a high U-value may cause considerable heat loss as well as increased energy usage. This may be especially troublesome in cold areas, where heat loss through the floor can be a significant source of energy waste. Besides that, a high U-value slab can cause thermal discomfort for people since cold floors are uncomfortable to walk on and can provide an unpleasure environment.

Six alternative slab assembly construction options were explored using the best-case model from the roof construction simulations (Form baseline Rectangle with 20% WWR, a frame wall construction 2013 External Wall, and 2013 Roof). According to IECC 2021, the maximum F-factor requirement for unheated slabs in climatic zone 1A is 0.73. According to the iterations result, the APSOG111(F-factor 0.73, U-value 0.505 W/㎡K), has the lowest energy consumption (EUI 71.03 kWh/sqm/year) and carbon emission(213058 kg). It is interesting to note that the U-value of this slab is not the highest or lowest, but a somehow middle value. Maya explored the reasons for this occurrence, and Maya thinks that in hot climates, underfloor and slab insulation may not always be advantageous for a few reasons:

1. Heat gain: Insulating the slab can keep heat from exiting the structure, but it can also keep heat from escaping into the ground, which can lead to overheating. Allowing heat to drain into the ground can assist to lower the building’s cooling load in hot areas since the ground is frequently cooler than the air. Insulating the slab may also trap heat within

For cold regions, the lower the U-value of slab helps to reduce the EUI and get better energy efficiency. However, for hotter regions, the U-value and material should be selected depending on the local soil conditions, temperature, etc.

4.5 Glazing

Window glazing is the glass inside of a window, which can be single, double, or triple glaze (also known as single pane, double pane, or triple pane).

Six alternative window glazing assembly construction options were explored using the best-case model from the slab construction simulations (Form baseline Rectangle with 20% WWR, a frame wall construction 2013 External Wall, 2013 Roof, and 2013 Exposed

Table 4.4.1 Pittsburgh - Baseline - U-VALUE: 0.256

Table 4.4.2 Miami - Baseline - U-VALUE: 0.505

Floor). According to IECC 2021, the maximum U-value of climatic zone 5A fenestration is 2.04 W/㎡K, and the maximum SHGC is 0.38(PF<0.2, fixed).

According to the iterations result, the 2013 External Window (U-value 1.68 W/㎡K) has the lowest energy consumption (EUI 68.37 kWh/sqm/year) and carbon emission(221492 kg).

Six alternative window glazing assembly construction options were explored using the best-case model from the slab construction simulations (Form baseline Rectangle with 20% WWR, a frame wall construction 2013 External Wall, 2013 Roof, and APSOG111 slab). According to IECC 2021, the maximum U-value of climatic zone 1A fenestration is 2.84 W/m^2`K, and the maximum SHGC is 0.23(PF<0.2, fixed).

According to the iteration result, the AG-EXT3(U=0.50; SHGC=0.22; VT=0.242) has the lowest energy consumption (EUI 69.71 kWh/sqm/year) and carbon emission(209085 kg). Compared to baseline 2013 External Window and other iterate selections, neither SHGC nor U values are the lowest, but they work

Both SHGC and U-values are important for the design of windows in buildings. A higher SHGC means that more solar radiation passes through the windows, which is beneficial in cold climates where passive solar heating can be utilized but may result in excessive heat gain and increased cooling loads. A lower U-value means that less heat is transferred through the window, which can be beneficial in both cold and hot climates to reduce the building’s heating and cooling loads. Therefore, in hot climates, it is often recommended to choose windows with lower SHGC values to help reduce solar heat gain, and improve energy efficiency, on contrary, higher SHGC sometimes can be

5. Building Component Iteration and Comparison Summary

To sum up, our iteration of building components lowers the EUI as shown in the following two figures.

Using rectangle building shape, the EUI of our building in Pittsburgh will be lower if the

Table 4.5.1 Pittsburgh - Baseline - U-VALUE: 1.680

Table 4.5.2 Miami - Baseline - U-VALUE: 2.839

window-to-wall ratio is changed from 40% to 20%. This is because less WWR can result in less heat loss. Also using rectangle building shape, the EUI of our designed building in Miami will be lower if the window-to-wall ratio is changed from 40% to 20% and when

Figure 5.1. Energy Consumption

Figure 5.2. EUI Comparison

CHAPTER II. TCS HALL MODELING

1. Executive Summary

This report presents the modeling and modification of a building energy model with the aim of achieving energy conservation and carbon emission reduction. The baseline model was developed by IESVE based on the geometry model provided by our group and taking the building type and climate zone into account. The proposed model was then developed by us through modifications to the building envelope structure parameters, window-to-wall ratio (WWR), indoor lighting, and other factors.

The first section of the report provides details on the baseline model, including its overall characteristics. Our group created the baseline model based on the TCS Hall building located in Pittsburgh, PA, under the 5A ASHRAE climate zone.

In the second section, as we made some design alternatives to the baseline model, we provide details of the modifications. These modifications include changes to the building envelope, internal gains, and operational schedules. We compare and contrast the data from the baseline model to the modified model based on the results of each group of modifications.

Our modifications resulted in a 15.71% reduction in total annual energy consumption, with reductions of 16% in heating, 5.8% in interior lighting, 23% in space cooling, 39% in fans, 15.4% in the pump, and 23% in heat rejection. We earned 5 points for the LEEDv4 EAc1 credits.

By targeting heating as the primary area for design improvements, we reduced the CO2 emissions from 16.37 kg/m2/year in the baseline model to 13.04 kg/m2/year in the modified model. This reduction represents over 20% of the total CO2 emissions reduction.

The last sections of the report include our concluding statement regarding this project, our group work log, and all of our references.

2. Baseline Building

2.1 TCS Hall Basic Information

2.1.1 Building Information

TCS Hall at Carnegie Mellon University was originally built in 1916 and was used as an office building until it underwent a complete renovation in 2018. It is now home to the School of Computer Science, the Human-Computer Interaction Institute, and the Language Technologies Institute. The primary building programs are centered around a central atrium that is flooded with natural light. The north side of the building houses classrooms and lecture halls, while the east side is dedicated to faculty offices and research labs. The south side of the building contains collaborative spaces, including a large conference room and an open work area. One of the standout features of TCS Hall is the design of the central atrium. A glass roof and skylight flood the space with natural light, and a large circular staircase connects the two floors. The staircase is designed to encourage collaboration and interaction, with seating areas on each level and an open view of the entire space.

TCS Hall is designed with sustainability in mind, achieving LEED Gold certification for its energy-efficient

design, which includes a green roof, energy-efficient HVAC systems, and water-saving fixtures.

2.1.2 Model Assumptions

The TCS Hall building, which is an office and laboratory building with five stories and a semi-overhead parking lot, was the subject of our model for this report. But since we do not have access to the fifth floor’s and the parking lot’s floor plan, we only modeled level 1-4 of the building.

Our building’s total area is 72651.90 ft2 and our building’s volume is 942335.13 ft3.

We utilized weather data from Pittsburgh, PA, situated in the ASHRAE climate zone 5A, for our model's weather file.

TCS Hall Floor Plans (Level 1-4)

2.1.3 Baseline Model Construction

We opted for non-residential building components, including roofing, exterior walls, ground slabs, and glazing materials, that conform to the regulations outlined in the ASHRAE 90.1 2016 User Manual for our 5A climate zone as our baseline construction materials.

The table below detail the specific materials utilized for our baseline model.

Model view in IESVE

Table2.1.1 ASHARE 90.1 Standard for Fenestration

2.2 Thermal Information

2.2.1 Thermal Properties

To establish our baseline construction materials, we opted for non-residential building components that complied with the guidelines outlined in the ASHRAE 90.1 2016 User Manual for the 5A climate zone. We identified the specific materials to use for our baseline model, and we have included these in the tables provided below.

2.2.2 Thermal Zoning

Though TCS Hall has a lot of separate rooms, most of them are in parallel and have similar functions. Thus, we divided the rooms into six thermal templates, spaces that have similar usage patterns are given the same thermal template, which determines their internal loads and air exchanges based on the ASHRAE 62.1-2019.

Also, considering the issue of infiltration, some rooms with same function, such as the office, was divided into two different templates, named enclosed and open-plan.

Below is the detailed area in square feet in each divided thermal template:

Table2.2.1 Material and Construction detail of the Baseline Model

Table2.1.2 ASHARE 90.1 Standard for Opaque Elements

Dividing a building into HVAC thermal zones is a crucial step in designing an effective and efficient heating, ventilation, and air conditioning (HVAC) system. In the case of TCS Hall, we divided the Thermal Zone based on several considerations to ensure optimal performance.

Firstly, we identified the different areas of the building that have varying heating and cooling requirements. Then, we considered the occupancy and usage patterns of the different areas of the building, grouping together those with similar usage patterns into the same thermal zone.

As a multi-functional office building, TCS Hall includes office spaces, a lobby, and corridors. To account for this, we divided the office areas into enclosed and open spaces and separated the functional areas such as restrooms, stairs, and elevators. The primary usage areas require a higher level of thermal comfort for occupants, and the HVAC system should be able to adjust according to occupancy demand, while the functional areas have lower requirements and do not need to be personalized to meet occupancy demands.

Next, we analyzed the building's orientation. Pittsburgh, where TCS Hall is located, is in Climate Zone 5A according to ASHRAE's climate zone map. In this climate zone, the heating season is longer than the cooling season, and winters are generally cold and snowy, while summers are mild to warm. Therefore, we considered the south-facing facade's direct solar gain, which can help reduce heating demand and provide natural daylight in the winter.

We also evaluated the building's envelope, including walls, roofs, and windows, which can affect heat loss and gain. Areas with different envelope characteristics were separated into different thermal zones.

Finally, we evaluated the HVAC system to determine the best way to divide the building into thermal zones, taking into account the location of air supply and return ducts and the system's capacity.

In conclusion, TCS Hall's narrow and elongated shape and external building envelope differences between the east and west facades were considered when dividing the building into thermal zones. By taking these factors into account, we ensured that the HVAC system was designed to provide optimal performance and comfort for occupants.

Below is the detailed thermal zoning labeled in colors.

The building's primary use is for office spaces, and due to the different occupancy schedules and thermal comfort preferences, a personalized HVAC system is required. Therefore, independent air handling units (AHUs) for each floor are recommended, with VAV terminals to meet the needs of different rooms. It is necessary to consider the path of ductwork and piping to avoid occupying the ceiling space in the rooms. According to the building's floor plan, there are two main corridors on floors 2-4, and the rooms are divided according to the east-west orientation, which considers room loads and meets the needs of the duct and pipe layout. So, we divided each floor's corridor separate zones, but it may be adjusted based on factors such as the chosen air supply outlet. For the first floor, it is mainly considered the space type.

In addition, dividing the stairs into a separate thermal zone can lower the temperature requirements and facilitate the setting of fire protection required supply and exhaust air control. Dividing the restrooms into a separate thermal zone can also lower the temperature requirements and facilitate the setting of ventilation facilities.

Besides, we divided the restrooms into a separate thermal zone to lower the temperature requirements and facilitate the setting of ventilation facilities.

After referring to the layout of the building shafts, we speculated that the actual HVAC zones of the building are divided into north and south zones, which may result in shorter air duct paths. However, some rooms may have reduced net ceiling heights due to the need to install air ducts.

The zoning plan is as follows:

Thermal Zones Division Diagram

2.2.3 Thermal Template

To build the thermal templates, we used the ASHRAE 90.1-2016-based IES-VE templates as a starting point for the baseline model.

Below is a list of our assigned thermal zoning and thermal templates of the spaces in our model:

Table 2.2.3 Thermal templates for the assigned thermal zones

2.3 Baseline Energy Performace

In the baseline model, the building's annual energy consumption was 5246349.80 kBtu/yr, with an energy use intensity (EUI) of 72.21 kBtu/ft²/year. The energy use was divided into gas energy, which amounted to 2032200.50 kBtu, and electricity, which amounted to 3214149.30 kBtu. The higher consumption of gas energy can be attributed to Pittsburgh's climate, which is primarily heatingdominated. The building relied heavily on natural gas as a fuel source for heating, resulting in a greater demand for gas energy compared to electricity.

The following is the load results and energy usage percentage breakdown:

Figure 2.3.1 Annual cumulative energy use disaggregated by fuel source of Baseline Model

3. Design Alternatives

Rather than making iterative changes one by one, we employed a method where we made a single version of an iteration of the building and generated a result after obtaining the instructor's approval. Our goal was to investigate how alterations to different aspects of the building could affect the energy simulation outcomes. We focused on four categories of design exploration, which included envelope geometric properties, envelope thermal properties, internal loads, and operational schedules. To test a range of design alternatives and create an improved building in terms of energy usage, we performed three parametric simulations for each category using a step-by-step approach. The aim was to identify the best design alternative that would optimize the building's energy efficiency.

The effect of building loads can be influenced by several parameters, including the building's orientation, envelope design, glazing systems, shading devices, occupancy patterns, and building systems such as HVAC and lighting. For example, a building with a high window-to-wall ratio (WWR) may have higher cooling loads due to increased solar gain through the windows, while a building with a low WWR may have higher lighting loads due to less natural daylighting. Building loads can also be affected by the climate and location of the building, as well as the type and efficiency of the building systems used.

By understanding the effect of building loads and optimizing building design and systems accordingly, it is possible to reduce energy consumption and improve the overall energy performance of a building. This can lead to cost savings, improved comfort for occupants, and reduced environmental impact.

3.1 Envelope Geometric & Thermal Properties

In this section of the report, we aimed to evaluate the energy performance of TCS Hall building located in climate zone 5A, specifically focusing on the impact of the building envelope design. To achieve this, we conducted simulations to assess the effect of various modifications made to the wall, roof, slab,

glazing assemblies, and window-to-wall ratio (WWR). Our goal was to determine the most effective modifications to achieve energy efficiency and compare the results with the baseline model for further analysis.

3.1.1 WWR

The WWR, or window-to-wall ratio, has a significant impact on building loads and energy use. In the case of TCS Hall, the WWR is relatively high, and combined with the climate conditions of the building location, we determined that the WWR should be appropriately reduced. Our recommendation is to keep more windows on the southern side, where they can receive direct sunlight in the winter and reduce heating loads. WWR on other orientations should be reduced accordingly. After reducing the WWR to an overall ratio of 22% (North 19%, South 26%, East 21%, West 21%), we were able to achieve a 6.91% reduction in overall energy use.

3.1.2 Ground Slab

To improve the performance of the ground slab, we made several modifications, including changing the insulation materials and increasing the slab's thickness. We consulted various reference materials and based on our analysis, we determined that the recommended construction has a lower U-value compared to the baseline construction.

Table 3.1.1 WWR compare(baseline model and proposed model)

Table 3.1.2 U-Value of Ground Slab

Construction layers diagram of slab

However, during the simulation, we discovered that the performance of the slab was not optimal, and it actually increased energy consumption by 12.78%. This is similar to the results we obtained in Lab1, which was conducted in Miami. The issue may be related to the balance of heat transfer between the ground and indoor space during both winter and summer months. It's possible that there is an optimal range of U-value for the slab that is specific to certain regions, but unfortunately, we were unable to identify it. As a result, for the subsequent simulations, we opted to use the ASHRAE ground floor parameters, as follows:

3.1.3 External Wall

The performance of exterior walls affects the insulation effect of buildings. From the exploration of Lab1, we learned that the U value of walls is determined by their material and thickness. After consulting some reference materials and construction atlases, we found that modifying the insulation layer is the main strategy to improve the energy performance of exterior walls in the location of this building because it is the biggest factor determining the R-value of the exterior wall. After changing it, the total energy use reduced 3.18%.

In the material selection, we compared the R values of different insulation materials and, combined with data and common constructions, we chose Cavity Insulation. By filling the cavity between the inner and outer walls with insulation material, heat transfer through the wall can be significantly reduced, which helps to keep the building warmer in winter and cooler in summer. The detailed information of wall construction is shown in the table below:

Table 3.1.4 U-Value of External Wall

Table 3.1.3 Construction Layers of Ground Slab

Construction layers diagram of external wall

3.1.4 Roof

The U-value of a roof can be improved by using insulation materials with a low thermal conductivity. Increasing the thickness of the insulation layer can also reduce the U-value. After changing it, the total energy use reduced 1.13%.

The modified roof can reduce energy consumption by %, and the selected roof parameters are as follows:

Construction layers diagram of roof

Table 3.1.5 Construction Layers of External Wall

Table 3.1.7 Construction Layers of Roof

3.1.5 Glazing

Glazing is an important element that affects the building loads, particularly the heat gain and loss through the building envelope. Windows or glazing assemblies have different U-values and solar heat gain coefficients (SHGC) which influence the amount of heat that passes through them. A higher U-value means more heat transfer, and a higher SHGC means more solar heat gain. Using new glazing helps us to reduce 4.64% the total energy.

By choosing the appropriate glazing assembly, it is possible to reduce the building's heating and cooling loads. Double or triple-glazed windows with inert gas-filled cavities can significantly reduce heat transfer through the window. These strategies can lead to a reduction in the building's energy consumption and lower HVAC system loads.

In Assignment 2, we gained a deeper understanding of the construction of glazing. Single-pane glass has poor thermal performance, which can result in high building loads. Therefore, we considered using triple-pane insulating glass, which has an air gap between the outer and middle glass layers to improve its thermal properties. Below are the parameters of the construction we selected. When choosing the glass, we also considered parameters such as Tvis and SHGC. In the region where the building is located, we wanted it to receive more solar heat gain during winter and more daylight to ensure a comfortable indoor lighting environment and reduce lighting energy consumption. Therefore, we aimed for a higher SHGC and Tvis.

Construction layers diagram of glazing

Table 3.1.9 Construction Layers of Glazing

Table 3.1.8 U-Value of Glazing

3.2 Internal Loads

3.2.1 Occupancy

We maintained the original schedule for the occupancy and did not make any changes to the daily schedule. This decision was made based on the fact that the building was a combination of a laboratory and an office building, and the actual operating hours of the building closely aligned with the existing schedule we set.

3.2.2 Interior Equipment

We also made sure that the interior equipment of the baseline model remained unchanged. When creating the baseline model, we had to consider various factors, and one of the most important was the internal heat gains from people, lighting, and computers or equipment. We included internal heat gains from people and lighting in all spaces, but only considered internal heat gains from computers in work areas and electrical/mechanical rooms.

3.2.3 Lighting

For the interior lighting control, with the default setting, the energy consumption of the proposed model is 63.2% greater than the baseline model. At first, we tried to use the scheduled shut-off strategy and automatic partial off. Although it improves the energy performance, the proposed model is still 14.6% greater than the baseline model. Thus, to improve energy performance, we tried to find a lighting control strategy to improve energy performance.

We found a field study conducted by Anca D, Galasiu in 2007. about lighting control methods.[1] The methods used to reduce energy consumption in this study include the integration of occupancy sensors, light sensors for daylight harvesting, and individual dimming controls for each workstation. These controls allowed for a reduction in lighting energy use during work hours and resulted in an overall savings of 42 to 47 percent compared to a conventional lighting system. The study also conducted an awareness campaign to encourage occupants to use the individual control feature of the lighting system.

From the article, the type of building that can use these methods for lighting control is any building with a similar lighting system to the one described in the study. So we highly recommend our clients use this system. Deep-plan office buildings with central cubicle workstations and suspended directindirect luminaires can benefit from the use of occupancy sensors, light sensors, and individual dimming controls. Perimeter workstations may benefit more from light sensors due to the availability of natural daylight. The use of these controls may result in energy savings, peak power reductions, and higher occupant satisfaction.

After applying this strategy, interior lighting energy use improved by 16.3%, which can save 1.7% energy usage comparing to the baseline model.

3.3 Operation Schedule

We maintained the original schedule for the operation and did not make any changes to the daily schedule. This decision was made based on the fact that the building was a combination of a laboratory and an office building, and the actual operating hours of the building closely aligned with the existing schedule we set.

[1] Galasiu, Newsham, G. R., Suvagau, C., & Sander, D. M. (2007). Energy Saving Lighting Control Systems for Open-Plan Offices: A Field Study. Leukos, 4(1), 7–29. https://doi.org/10.1582/LEUKOS.2007.04.01.001

3.4 Comparative Analysis of Building Energy Performance

The figure 3.4.1b provides a more detailed breakdown of the energy usage. Space heating accounts for 41% of the total annual energy consumption, and as the TCS building is located in the 5A climate zone with very cold winters, reducing heating loads can significantly improve the building's energy performance. Lighting is also an important target in our simulation. Initially, the proposed model consumed 60% more energy for lighting than the baseline, indicating a significant potential for improvement. By changing the lighting control mode, we were able to reduce energy consumption by 5.31% compared to the baseline. Additionally, we should consider reducing loads in other areas, such as space cooling and HVAC equipment. Energy consumption is directly related to carbon dioxide emissions, with gas consumption causing the most significant emissions. Therefore, we need to consider reducing the use of gas for heating. These areas can be further improved in subsequent simulations. We plan to achieve 20% energy reduce in Lab3 HVAC system design. Which may help us get 8 points form LEEDv4 EAc1 credits.

Table 3.4.1 Result summary of Proposed model

4. Comparative Analysis

4.1 Baseline model and best proposed model comparison

The table summarizes the energy consumption results of both the baseline model simulation and the best proposed model, the electricity usage decreased 15.54%, the gas usage decreased 15.95%.

Breakdown EUI of Proposed model

Breakdown percentage change of Proposed model

We successfully reduced the Site EUI by 20% and the CE by 25%, which is a good reference for improving the building's performance. If we want to further reduce the CE, we can consider reducing the heating loads, which means reducing the use of fossil fuels. And if we want to reduce the total energy consumption, we can also consider the electricity consumption caused by cooling and lighting.

The proposed design shows a lower cumulative energy consumption level compared to the baseline design, indicating that it is more energy-efficient. Disaggregated EUIs by end-uses for both designs reveal that the proposed design is more energy-efficient than the baseline design for heating, cooling, and lighting, while it uses slightly more energy for fans, pumps, heat rejection, and equipment. The proposed design uses less natural gas and electricity compared to the baseline design, with a difference of 9.3% and 7.3%, respectively. Moreover, the proposed design emits less CO2 compared to the baseline design, with a difference of about 7.7%.

Looking at the peak loads, the proposed design has a lower heating load but a slightly higher cooling load compared to the baseline design. This suggests that the proposed design may have better insulation and heating systems, but may require more cooling energy due to factors such as increased occupancy or more extensive glazing.

In conclusion, the proposed design is more energy-efficient and environmentally friendly than the baseline design, although there may be trade-offs in terms of cooling energy and equipment usage.

5. Conclusion

Moving forward, the goal is to achieve a 20% energy reduction in Lab3 HVAC system design, which may help to obtain 8 points from LEEDv4 EAc1 credits. The purpose of this study was to evaluate the effect of various building elements on energy usage using IES-VE simulation software. The study analyzed four specific design categories: envelope geometric properties, envelope thermal properties, internal loads, and operational schedules. The best-case building model demonstrated significant reductions in heating and cooling peak load as well as energy usage intensity.

However, several issues were encountered during the simulation. Firstly, some building drawings and envelope material parameters were missing, which may have led to significant deviations between the

Peak Load Comparison

simulation and actual building performance. To mitigate this, the model was simulated with available drawings and ASHRAE 90.1 parameters as a baseline. Construction drawings and data were also consulted to improve the building's performance. Despite these efforts, the performance improvement was deemed not significant.

Another challenge was setting up room templates and schedules. The simulation was difficult for rooms with unclear usage functions and high personnel flow. Due to the lack of schedules, field investigation and monitoring data were not available. Misunderstandings regarding the tasks caused time to be wasted on ineffective parameter changes and iterations, resulting in poor time management. Improving time management will be essential for future lab sections.

While the performance improved gradually during the simulation, there was still confusion regarding the deviation between the simulation results and expectations for the slab. The help from the teacher and TAs was appreciated.

6. Reference

[1] Galasiu, Newsham, G. R., Suvagau, C., & Sander, D. M. (2007). Energy Saving Lighting Control Systems for Open-Plan Offices: A Field Study. Leukos, 4(1), 7–29. https://doi.org/10.1582/LEUKOS.2007.04.01.001

[2] ASHRAE. ANSI/ASHRAE/IES Standard 90.1-2016: Energy Standard for Buildings except Low-Rise Residential Buildings (I-P Edition). Atlanta, GA :American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, 2016

[3] ASHRAE. ANSI/ASHRAE Standard 62.1-2019: Ventilation for Acceptable Indoor Air Quality. Atlanta, GA :American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, 2019

[4] Jie Zhao (5047391), Bertrand Lasternas (5047439), Khee Poh Lam (5047376), Ray Yun (5047427), & Vivian Loftness (5047436). (n.d.). Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining.

CHAPTER III. DESIGN BUILDER / ENERGYPLUS

This lab report aims to provide a detailed overview of the HVAC system design of our assigned building TCS Hall in the Design Builder software and the thermal simulation engine of EnergyPlus . The lab exercise involves developing a complete component geometry of TCS Hall, creating two sets of construction assemblies for the base and proposed design cases, and comparing their energy performance.

Additionally, three HVAC system design alternatives are developed(CAV, VAV and GTHP), and their energy performance is compared. The report includes our process and decisions developing a thermal zoning layout, adjusting space functions, and considering the impact of site and weather data, building enclosure components, and internal loads on the energy performance of the building design case.

2. Baseline Model

2.1 Input Parameters

2.1.1 Space Functions_Internal Load Density, Occupancy and Schedule

As a baseline for building performance modeling of TCS Hall, we set . This data will be used to simulate the energy consumption and thermal comfort of the building under different scenarios.

Internal load density shows the amount of heat generated within each space of the building, expressed in watts per square foot. The highest load density is found in the offices, with a value of 0.46 W/ft2, while the lobby and atrium have no equipment load. The toilet area has the same load density as the stair area, with both having a value of 0.54 W/ft2.

Occupancy shows the number of people expected to be present in each space, expressed in square feet per person. The highest occupancy density is in the atrium, with a value of 100 ft2/person, while the lowest is in the lobby, with a value of 6.7 ft2/person.

The schedule data shows the hours of operation for each space. The lobby and office spaces are in operation from 8 am to 6 pm, while the other spaces are operational throughout the day. The office spaces are also divided into three typing categories, each with a different light load density and metabolic rate.

2.1.2 Thermal Zoning

In Lab 2, we classified the zoning according to various room types. However, our simulation showed that there were significant variations in the loads between the eastern and western parts, which could lead to an imbalanced system if we relied solely on one set of heating and cooling sources.

Consequently, for Lab 3, we opted to divide the zones based on their loads. This meant dividing them into northern and southern sections, with the exclusion of the Lobby, atrium, and toilets, which all have distinct functions.

Thermal Zones Division According to Room Types

Thermal Zones Division Based on Loads

2.2 Material Properties

For the thermal properties, we still used the data provided from the ASHRAE 90.1 2016 User Manual.

3. Proposed Model

3.1 Material Properties

In this section, an alternative building envelope is introduced to both CAV and VAV systems. The goal of this modification is to improve the building’s overall energy performance. The baseline model is built according to the CZ5 Non-residential construction template of ASHRAE 90.1 2016, and the proposed model is built based on the best-case scenario in assignment 2. A detailed construction assemblies comparison is shown below. Construction layers

Total thickness(in) U‐value(Btu/h*ft2*°F) Construction layers

Total thickness(in)

0.75in Stucco

1.7550 Glass fiber board insulation

2.000in Brickwork outer 0.8130in Cemnet and render

4.000in Cavity insulation

6.000in Concrete reinforced external wall 0.8130in Gypsum board

0.8130in Cemnet and render  7.602in EPS

0.8130in Cemnet and render  0.8130in Cemnet and render

4.8130in Concrete reinforced

12.000in Soil at R‐0.104/in

0.8130in Cement and render

10.000in Concrete reinforced

10.000in EPS

3.190in Loose fill/powders‐gravel 1.000in Aerated concrete slab

Table 2.2.1 Thermal Properties of Builidng Assemblies

Table3.1.1 Construction Details Comparison between Baseline Model and Proposed Model

As we can see, four kinds of assemblies are modified, external wall, flat roof, ground floor, and glazing. In order to increase R-value and decrease the U-value, the thickness of construction layers was added and materials are changed into stronger ones, especially the insulation layers. As a result, the total U-value of each proposed assembly is much lower than the baseline.

As for glazing, although the generic clear glass has a higher SHGC, its U-value is too high. Compared with this, the proposed double-glazing glass with a 13mm air layer in the middle decrease the U-value greatly without changing the SHGC too much.

3.2 Overall Results

The figure below shows the comparison of site and source EUI between baseline and proposed model. For the CAV system, the annual site EUI of the baseline model is 120.89 kBtu/ft2, while the propsed model's site EUI is 122.67kBtu/ft2. As for the VAV system, the annual site EUI of the baseline and proposed model are 100.12kBtu/ft2 and 107.58kBtu/ft2 respectively.

From the perspective of energy consumption, it is clear that the VAV system is significantly better than the CAV system. However, although in theroy, the energy performance of the proposed envelope should be much better than the baseline, the result is the opposite. The reason will be analyzed in the conclusion section.

4. HVAC Systems

4.1 CAV reheat with a boiler and air-cooled chiller

The CAV system works by supplying a constant volume of air to the space at a constant temperature. The air is distributed throughout the building through a network of ducts and registers, and the temperature is controlled by adjusting the amount of heating or cooling supplied to the air. One of the primary advantages of CAV systems is that they are relatively simple and easy to install and maintain. However, they can be less energy-efficient than other types of HVAC systems, as they do not adjust the amount of air supplied to the space based on occupancy or other factors.

For CAV system, we keep the default settings and let EnergyPlus auto-sized it, below is the System block diagram. The SVG file can be found in the attached file.

4.2 VAV terminal reheat with a boiler and water- cooled chiller

The VAV system works by supplying a variable volume of air to the space at a constant temperature. The air is distributed throughout the building through a network of ducts and registers, and the temperature is controlled by adjusting the amount of heating or cooling supplied to the air. One of the primary advantages of VAV systems is that they are more energy-efficient than CAV systems, as they can adjust the amount of air supplied to the space based on occupancy or other factors.

In this design, the primary/secondary system is not used because the floors are not high and do not involve a great imbalance of load on different floors.

At first, we met the problem of not meet set point hours exceeding 300 hours per year. We tried tons of strategies to fix this problem, including changing the Cooling towe setting and using virtual partitions. At last, we found that improving the reheat allowed maximized set temperature can effectively help us to reduce the not meet set point hours but with only slightly increased energy.

The detailed AHU setting can be found next 2 pages.

Below is the VAV System block diagram. The SVG file can be found in the attached file.

Below is the VAV System setting for Chiller and Boiler, most of the settings are default, sizing factor based on real project design choose 1.25.

Below is the VAV System AHU setting. The final Max reheat temperature is 107.60 F.

4.3 GTHP (Geo-thermal Heat Pump) with a chilled beam and heated floor system

In this design, we also attempted to use a GTHP (Geothermal Heat Pump) with a chilled beam and heated floor system. The heated floor system uses radiant heat transfer to warm the interior of the building. Due to the difference in radiant heat transfer, the indoor set point can be lowered by approximately 2 degrees Fahrenheit compared to other convective heating methods, while maintaining similar thermal comfort.

The heat pump system has good energy-saving potential. Initially, the system selection was determined based on the load, but we found that, possibly due to factors such as soil temperature and the number of boreholes, there were a lot of "not meet set point" hours. Therefore, we expanded the system selection from and increased the size of the boreholes.

Below is the GTHP System block diagram. The SVG file can be found in the attached file.

As we can see, with an increase in the number and depth of boreholes, the number of not meet set point hours is gradually decreasing, but even we use 60 holes with Carrier830, it still cannot reach the ideal state, especially during the winter. Therefore, we speculate that additional equipment, such as a boiler, may be necessary to supplement the use of geothermal heat pump systems in the Pittsburgh area. It should be noted that since the heating floor system requires the addition of an internal source, minor adjustments were made to the baseline construction parameters.

For detailed information on the HVAC system settings, please refer to the table on last page.

5. Comparative Analysis

In this comparative analysis, we evaluated three HVAC systems we designed for the TCS Hall, mentioned in the previous section, based on energy end-use, seasonal, monthly, and annual energy consumption, peak load, and system capacity control capabilities.

The aim was to determine the most efficient and cost-effective HVAC solution for the building. This analysis provides valuable insights for building owners and operators seeking to optimize their HVAC systems for energy efficiency, cost-effectiveness, and occupant comfort.

5.1 Baseline CAV vs. Proposed CAV

The breakdown of the annual energy use intensity (EUI) of the baseline and proposed CAV systems shows that there is not a significant difference in their energy consumption for heating and cooling. However, there is a slight increase in the proposed CAV system's heating energy use compared to the baseline. Additionally, the energy consumption of fans in the proposed CAV system is slightly higher than in the baseline system. Other categories, such as interior lights and equipment, have similar energy consumption in both systems. These results suggest that the proposed CAV system does not provide significant energy savings compared to the baseline system. Further analysis may be necessary to identify potential improvements in the HVAC system design to achieve more significant energy savings.

The two graphs below are the monthly energy usage breakdown generated by the design builder, which reflects the energy usage deviation across the year. We can also indicate that there is not much difference between the monthly usage of baseline CAV and proposed CAV system.

Figure 5.1.1 Comparison of Annual EUI Breakdown of Baseline and Proposed CAV

The following figure shows the comparison of the annual EUI breakdown and total EUI of the baseline CAV and proposed CAV models. The baseline CAV EUI is indeed 120.59 kBTU/ft2, while the proposed CAV EUI is 122.67 kBTU/ft2. As we can see from the graph, the proposed CAV system has a slightly higher EUI due to the changes in construction materials, which led to an increase in heating and cooling loads. Due to the time limit, we didn't figure out the exact reason for this but we indicate that the increased heating load is likely caused by either the use of less insulated walls and windows, while the increased cooling load may be due to the decreased shading provided by the new glazing system, or maybe it's because of the floor material as we also mentioned during our presentation. It would be super helpful if the teaching faculty could help us figure out the reason. .

As introduced in the previous section, our decision to change the construction materials was based on their thermal properties, such as thermal conductivity, thermal resistance, and heat capacity, which affect the amount of heat transfer through the building envelope. From the simulation results, we realized that by selecting construction materials with better thermal properties, the building's heating and cooling loads can be reduced, resulting in lower EUI.

It is also worth noting that the change in construction materials could also affect other building parameters, such as the building's structural integrity, indoor air quality, and acoustic performance. Therefore, it is essential to consider all these factors when selecting construction materials for future energy-efficient building design.

Figure 5.1.2 Monthly Energy Usage for baseline CAV

Figure 5.1.3 Monthly Energy Usage for Proposed CAV

Figure 5.1.4 Comparison of Total Annual Energy Use Intensity Baseline and Proposed CAV

5.2 Baseline VAV vs. Proposed VAV

The proposed VAV system shows an improvement in overall energy efficiency compared to the baseline VAV system. The reduction in heating energy usage is particularly notable, decreasing by 6.82 kBTU/ ft2. However, the proposed VAV system has a slightly higher cooling energy usage, which could be due to the trade off between glazing SHGC and U value. The proposed VAV system also increase energy consumption from pumps by 8.28 kBTU/ft2 and energy consumption from fans by 3.1 kBTU/ft2. These improvements in heating performance may be attributed to the change in construction materials and the use of more efficient AHU settings in the proposed VAV system.

As in the graph, the proposed VAV system shows an increase in annual total EUI of 7.5 kBTU/ft2 compared to the baseline VAV system. Similar to the issue in the. previous section, the increase in energy usage is unsolved and we indicated that this may be attributed to the iteration of construction materials and equipment, such as insulation materials and HVAC settings.

In all, as we aimed to demonstrate an improvement n energy efficiency for the proposed VAV system, we failed to achieve our initial goal for some reason. But still, our experiment showcases the importance of careful design and material selection in creating more sustainable and energy-efficient buildings.

Figure 5.2.1 Comparison of Annual EUI Breakdown of Baseline and Proposed VAV

Figure 5.2.2 Comparison of Total Annual Energy Use Intensity Baseline and Proposed VAV

The two graphs above are the monthly energy usage breakdown generated by the design builder, which reflects the energy usage deviation across the year. Similar to the circumstance for CAV, there is not much difference between the monthly usage of baseline VAV and proposed VAV system.

5.3 Proposed CAV vs. Proposed VAV

From the site EUI data, we can see that the CAV system has a higher energy consumption than the VAV system for both the baseline and proposed models. Despite both systems experiencing an increase in the site EUI, the VAV system is still more energy-efficient than the CAV system. This can be attributed to the VAV system's capability to control the airflow to specific zones and adjust to the varying cooling or heating load in different areas of the building. In contrast, the CAV system supplies a constant airflow to the entire building, regardless of individual zone requirements.

However, when looking at the source EUI, we can see that the baseline CAV system has a lower energy consumption compared to the baseline VAV system. This indicates that the CAV system is more efficient in terms of energy conversion.

On the other hand, the proposed VAV system has a significantly higher source EUI than the baseline VAV system, indicating a decrease in energy efficiency. This could be due to the changes made in the proposed model, such as the increased glazing area and changes in HVAC settings. It is possible that the increased glazing area could lead to more heat gains and losses, requiring more energy to maintain indoor comfort conditions. Additionally, changes in the HVAC settings may have also impacted energy consumption.

Figure 5.2.3 Monthly Energy Usage for baseline VAV

Figure 5.2.4 Monthly Energy Usage for Proposed VAV

Figure 5.3.1 Comparison of Annual Site EUI of CAV and VAV Figure 5.3.2 Comparison of Annual Source EUI of CAV and VAV

5.4 Baseline GTHP vs. Proposed GTHP

The graph above shows the breakdown of energy usage intensity (EUI) for the baseline GTHP and the proposed GTHP system. The proposed GTHP system has higher EUI values than the baseline system for heating, cooling, and pumps. This is likely due to the proposed GTHP system's larger capacity and improved performance compared to the baseline system, which resulted in higher energy consumption. The proposed GTHP system has the same energy consumption as the baseline system for fans, interior lights, and interior equipment.

From a macro perspective, the proposed GTHP system has a slightly higher annual total EUI than the baseline system, with values of 19.61 kBTU/ft2 and 18.68 kBTU/ft2, respectively. However, this difference is relatively small, and it is possible that the proposed GTHP system may still be more energyefficient than the baseline system in practice due to its improved performance and capacity.

The two graphs above are the monthly energy usage breakdown generated by the design builder that reflects the energy usage deviation across the year. As seen in the graph, the energy usage in each month remains almost same for baseline and proposed GTHP.

Figure 5.4.1

Comparison of Annual EUI Breakdown of Baseline and Proposed GTHP

Figure 5.4.2

Comparison of Total Annual Energy Use Intensity Baseline and Proposed GTHP

Figure 4.4.4 Monthly Energy Usage for Proposed GTHP

Figure 4.4.3 Monthly Energy Usage for baseline GTHP

6. Conclusion

The project aims to optimize the whole building energy performance of TCS Hall by using Design Builder v8.7 and Energy Plus v9.3. To conduct a compariosn and interative design process, an auto-sized baseline CAV model is firstly set up according to ASHRAE 90.1 [2016] standard. Following that, three alternative models are developed with modified HVAC system and enclosure component. It is found that the VAV system is much more efficient than CAV sytem and the changing of HVAC system results in 17.2% reduction in total site EUI. However, although the thermal performance of enclosure components are increased, the annual totoal energy consumption slightly increases by 1.45%.

It is proposed that the simulation is a multifactorial process and the overall result is a combined effect of all the parametrics of different assemblies. From our multiple trials, we found that the order of the ground floor material layers and the comprehensive performance of glazing system are the most uncontrollable factors that could affect the energy consumption. Although the U-value has already been the lowest, the other elements may trade off the improvement.

7. Reference

[1] ASHRAE 90.1 – 2013/2016 "Energy Standard for Buildings Except Low-Rise Residential Buildings"

[2] Instructor's Powerpoint

[3] Omar's Video

[4] Galasiu, Newsham, G. R., Suvagau, C., & Sander, D. M. (2007). Energy Saving Lighting Control Systems for Open-Plan Offices: A Field Study. Leukos, 4(1), 7–29. https://doi.org/10.1582/LEUKOS.2007.04.01.001

CHAPTER IV. SOLAR PV MODELING

Properties of the Solar Photovoltaic System Designed for the Proposed Net-Zero Energy Building

Solar Power System Design Properties

Total PV peak power (at STC1): 12,950 Wp (12.95 kWp)

Total PV area: 78.71 m2 (847.24 ft2)

Total number PV modules: 70

PV-module Type and Specifications:

Manufacturer and Module Type: Solar World -SunModule SW 165/175/185 MONO

Solar Cell: Mono-crystalline silicon (Msi)

Number of Cells: 72 (125mm x 125mm)

Total Active Module Area: 1.125m2

Maximum Power: 185Wp

Module Efficiency: 16.32%

Open Circuit Voltage (VOC): 44.5V

Maximum Power Point Voltage (VMP): 36V

Short Circuit Current (ISC): 5.5A

Maximum Power Point Current (IMP): 5.1A

Temperature Coefficient of Short Circuit Current (A/K): 0.0033

Temperature Coefficient of Open Circuit Voltage (V/K): -0.1558

PV-performance model type in energy simulations:

Simple Performance PV Model (fixed cell efficiency and rated electric power output)

Power Conditioning Type:

DC-to-AC Inverters without a storage media, direct connection to utility grid with bi- directional electricity meter

Inverter Type: Simple Inverter Model (with 0.96 fixed efficiency)

Azimuth Angle: 180o (South)

Tilt Angle: Varies (28o rooftop PV array, 0o shading type PV array.)

Total annual photovoltaic power generation: 8,780.41 kWh (8.78 MWh)

Total annual solar power conversion loss: 702.43 kWh (0.70 MWh)

Total annual solar power generation on Site Energy Basis: 8,077.98 kWh (8.78 MWh)

Total annual solar power generation if supplied by the utilities on Source Energy Basis: 25,582.96 kWh (25.58MWh)

Total annual source energy saving by using on-site solar power generation: 16,802.55 kWh (16.80 MWh)

Total annual solar power generation intensity: 1.30 kWh/m2 (Building Area: 6361.00m2)

Percentage of electric loads satisfied: 0.70%

Building Integration Types:

Roof-top solar PV arrays, Shading-device (overhang integrated) type solar PV arrays.

Solar PV Array 1 Properties Roof-top solar PV arrays: 44.98m2, 40 PV Modules, 7400Wp, 180o azimuth angle, 28o tilt angle, 1.2m spacing between each PV row, 1.2m setbacks from the edges of the roof perimeter.

Solar PV Array 2 Properties Shading type solar PV arrays: 33.73m2, 30 PV Modules, 5550Wp, 180o azimuth angle, 0o tilt angle (horizontal).

Total Estimated Cost (all system components): N/A