Overheating in Buildings by Lizzi Twells

Using simulation programs to predict

Overheating in Buildings Elizabeth Parkinson MArch Architecture Year 2 Lecturer: Rory Jones

Contents

Introduction

Context

Building Introduction

Building the Model

Evaluation of Results

Critical Review

Conclusion

References

Appendix

Introduction The purpose of this report is to assess and analyse the data produced from a simulated model with its primary focus being air temperature. It has been built in the ‘DesignBuilder’ program. The report uses a block of flats that are located in Torquay, Devon UK. They where built to meet the criteria set out by the Code for Sustainable Homes. Within this report the actual and predicted data is compared and reasons for the ‘performance gap’ are discussed.

Types of homes that are overheating Living room 5%/25oC

Living room 1%/28oC

Percentage of homes over the acceptable threshold

30 22

20 10

7 3

0 Detached

Semi-‐detached

End terrace

Mid terrace

Flat

Dwelling type

Bedroom 5%/24oC

Bedroom 1%/26oC

80 74 71

Percentage of homes over the acceptable threshold

36 32 28

30 22

0 Detached

Semi-‐detached

End terrace

Mid terrace

Flat

Dwelling type

Sources: Module and overheating introduction lecture by Rory Jones. Beizaee A, Lomas KJ, Firth SK. National survey of summertime temperatures and overheating risk in English homes. Building and Environment 2013;65 : 1 - 1 7 .

Context What is overheating? Comfort is a measure of overheating. It is rated on a scoring system where a participant rates 1 - 5 depending on how comfortable they are. This is of course subjective. Comfort itself is not defined but for the purpose of this report I will use CIBSE Guide A. The guide states that no more than 5% of occupied time in a lounge should be over 25 oC and no more than 1% of occupied time should be over 28 oC. For the bedroom no more than 5% of occupied time should be over 24 oC and no more than 1% of occupied time should be over 26 oC. “Others have used the same criteria to evaluate indoor temperatures, for example, Wright et al., (2005) used hours over 25 oC and 28 oC in their study of temperatures during the 2003 heat wave. In their modelling study, Peacock et al. (2010) use 28 oC as a threshold for living rooms and 23.9 oC at 23:00 for bedrooms, and in their modelling work, Hacker et al. (2008) deemed that a building was overheated if in any year more than 1% of occupied hours exceeded 28 oC for living rooms and 26 oC for bedrooms”. 1 The aim of comfort is to reduce an occupant’s dissatisfaction and to ensure indoor conditions promote good health, not just avoid illness. 2 The thermal environment, as defined by CIBSE Guide A, has four sub categories: air temperature, mean radiant temperature, relative air speed and humidity. The aspect of comfort that this report focuses on is air temperature, as CIBSE lists air temperature as the most important. Why is this relevant now? The issue of overheating in new homes is particularly relevant now because of the relatively new (2008) section added to building regulations: Air Tightness Legislation Part L. Building regulations state: “It is defined as air leakage rate per hour per square metre of envelope area at a test reference pressure differential across the building envelope of 50 Pascal (50 N/m2)...The design air permeability is the target value set at the design stage, and must always be no worse than the limiting value.” 3 1 Extract taken from: Eppel H, Lomas KJ. Comparison of alternative criteria for assessing overheating in buildings. BRE Support Contract Report No. 12. Leicester: Scho ol of the Built Environment, Leicester Polytechnic (De Montfort University), UK: 1992 2 Chartered Institution of Building Services Engineers (CIBSE). Guide A, environmental design. 7th ed. London, UK: CIBSE 2006. [CIBSE Guide A]. 3 HM Government. Approved Document L1A: Conservation of fuel and power (New dwellings). HM Government 2010. Available at: http://www.planningportal.gov.uk/uploads/br/BR_PDF_AD_ L1A_2010_V2.pdf. [accessed 21.02.14].

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/6748/2173483.pdf

These regulations followed the development in building techniques (and building ‘trend’; Passivhaus) to control as much of the air flow as possible in and out of buildings. This has its own set of problems. Two significant issues are the end users’ knowledge of how to get the best from their home and the level of building precision that can realistically be achieved in the UK. The legislation has also incurred the problem of homes becoming too air tight, meaning they frequently overheat. This is a problem for low build-cost homes because the cheapest method of construction is timber frame which has a very low thermal mass. Consequently, the two factors combined, low thermal mass and higher levels of air tightness, means new homes are particularly susceptible to overheating. The evidence opposite shows the problem appears to be much worse in flats, probably due to the density of dwellings and rising heat. The percentage of households living in flats, maisonettes, apartments and tenements increased from 14% (3.1 million) to 16% (4 million) in the 2011 census. 4 More than one quarter of the population who live alone (26.2%), live in flats, units or apartments 5. Two common user groups of flats are young professionals and the elderly. Young professionals have been chosen as a scenario because, compared to the other user groups, they stay at home the least. A typical elderly or unwell person would stay in their flat most of the time. The elderly are increasingly 4 http://www.theguardian.com/housing-network/2012/dec/12/census-housing-at-a-glance 5 http://www.abs.gov.au/AUSSTATS/abs@.nsf/0/1EA78AFE3DE2EDCACA256BDA0073EB53?OpenDocument

Aging Population Current & Predicted

http://www.ons.gov.uk/ons/resources/populationagestructure198520102035final_tcm772- 34538.png

Both images from: Knowledge is Beautiful by David McCandless. Published: William Collins (25 Sept. 2014)

Actual & Predicted Temperature rise

http://www.epa.gov/climatechange/images/science/ScenarioTempGraph-large.jpg

important to consider when designing buildings as the UK has an aging population, (see graph on page 9). Overheating can cause respiratory and cardiovascular diseases which are increased in the vulnerable: elderly, ill and very young. At internal temperatures above 35 oC there is a significant danger of heat stress. Climate change Climate Change is also contributing to the impact of overheating in buildings. The increasing frequency of heat waves means that more people than ever are dying from overheating 6. In the summer of 2003 two thousand people in the UK died from overheating. At ‘best case’ scenario, temperatures are expected to rise by 2 oC in the next 17 years (see image bottom left). The MET office say that by the 2040’s the heatwave of 2003 mentioned above would be a normal summer for the UK 7. By 2080 the MET office also predicts an average summer temperature of 22 oC and an average winter temperature of 3 - 10 oC. These temperatures are comparable to areas like Aquitaine in France 8. 6 Module Handbo ok By Rory Jones 7 http://www.theguardian.com/environment/2014/mar/25/climate-change-uk-weather-wetdry-met-office 8 http://www.holiday-weather.com/country/france/

The vernacular architecture in Aquitaine (or any other area with a similar climate) has some features in common with that of Southern England. Some of the features of Aquitaine vernacular architecture are: clay tiles, high thermal mass, smaller windows and shutters, all aimed to reduce the internal temperature. New builds are also built with solar shading and natural means of cooling as a necessity (see images below and right). Even without focusing on the new legislation for air tightness, the UKâ&#x20AC;&#x2122;s new build typology needs to start adapting to the predicted climate changes, especially if â&#x20AC;&#x2DC;building to lastâ&#x20AC;&#x2122; is still considered a valid principle. Historic building methods usually took more energy to construct but can last hundreds of years with many typologies enabling adaptation to social and economic changes. Holistically, building to last is a more sustainable way to design and build.

Vernacular Aquitaine Architecture

http://imagesus.homeaway.co.uk/mda01/65fa6e85-dcce-421d-a69b-b35a6dfce5a1.1.10

Why is simulation important? Although the real use for simulation programs is often confused, they are an important part of the design process. Simulation programs should not be used to attempt to replicate the built building but to focus on the detail needed to produced the relevant data for the intended outcome. Simulation programs cannot precisely predict the internal conditions of buildings, even if every value has been entered into the model correctly, because there are so many unpredictable variables. The most effective use of simulation programs is to always use worst case scenarios as the programs tend to give optimistic results. During the design process simulation programs should be used to compare design alternatives, not just for their technical qualities but also how they sit in the landscape and the way they look in 3D. For example, different solar shading methods could be tried to estimate the most effective. Simulation programs can also be used to determine the meeting of legal targets, although the simulation can only ever indicate likely compliance.

Proposed New build Flats in Bordeaux, Aquitaine

http://photos.superimmoneuf.com/program/b/f/3/8/651693-biggest.jpg

South West Elevation of DesingBuilder Model

North East Elevation of DesignBuilder Model 14

Modelling software context Choosing a program depends on the capability of the user, the complexity of the project and the type of energy being assessed. To understand where a simulation programâ&#x20AC;&#x2122;s inaccuracies are it is important to outline how it works. Simulation programs use real gathered data and apply it to other variables. The difference between calculations by hand and calculations by a simulation program is the complexity. However, complex does not necessarily mean more accurate because replication of a minor mistake can escalate into a major inaccuracy. There are three basic levels of complexity: Stationary- one isolated event; Semi- Stationary - different steps of change, say over a month; and Dynamic - frequent samples over an extended period of time. An alternative to simulation is representation. This is when real situations are drawn in a simple way so viewers understand the principles. Representations do not produce accurate results, although neither do simulation programs. However, simulation programs produce results in situations where it would be too complex to use representation methods. Simulation programs are used in order to gain certification, from accreditation boards such as BREAM, LEED and CfSH. These accreditations are designed to improve the built environment but developers can also use them as a marketing tool. EnergyPlus is a tool that runs within DesignBuilder to give the simulation results. It is one of approximately 400 simulation tools in the Building Energy Software Tools Directory 9. EnergyPlus provides a whole simulation package, rather than an assessment of particular parts of the building or just one type of energy assessment. There are also some very complex engineering software programs that can process more complex results. The more complex the program, the more practice and training is required. Smaller easier to use programs, which architectural teams can operate themselves, are best placed to run simulations for small projects which keeps costs down. For larger, more complex and expensive projects it may be cost effective to pay a trained professional to produce the energy calculations. On large projects, using energy simulation programs increases the potential for saving money. Running costs can also be reduced in the same way. If engineers are required, they should be involved from the start of the design process to improve the project overall and potentially save time and money. DesignBuider was chosen because it is relatively easy to use, at a basic level and has a feature to focus on overheating. It also has a feature that allows Revit models to be placed into the interface. 9

Building Performance Simulation Lecture by Prof Peter de Wilde 2014

Description of Results Building Case Study The building analysed is located at Beechfield View, Torquay and was built in 2013. The developerâ&#x20AC;&#x2122;s aim was to ensure that the building was compliant with the Code for Sustainable Homes. The building has been described as the first large-scale eco-development in the South West. 1 The building comprises of twenty flats; those selected for analysis were chosen because they are the most likely to have problems with overheating. All the flats were on the top floor, with Flat 121 having the least exterior walls and Flats 118, 119 being south or south westerly facing (see plan view of Floor 3 below). Two types of simulation have been set up (shown on pages 20): 1. Best Case: occupants who are out most of the time, for example a young professional. 2. Worse case: occupants who are at home most of the time, for example an elderly or unwell person. These groups represent the extremes of typical flat occupancy, as discussed on pages 6-9. 1 http://www.tda.uk.net/latest-news/2014/4/7/torbay-s-award-winning-eco-developmentbeechfield-view-officially-opened-with-unveiling-of-sculpture-a378

Building the Model These are the variations which were noted down whilst the model was being built. Location and Weather Data The closest available location data was Plymouth Mount Batten but this only available by payment so Jersey was used. There is no weather data for Torbay so IWEC data was used from the US Department of Energy website 1. Materials The following assumptions were made for the individual materials used in the building construction. However, the simulation program does not accurately reflect the composition of the built components as it does not account for the complexity of construction in the actual building. For example, the stud work and insulation. Internal Partitions - Assumed glass fibre quilt roll as only roll insulation was specified. Roof

- Assumed ‘roofing slate’ no mineral fibre option - Wooden battens selected not tanalised battens – no tanalised option - Breathable membrane was assumed ‘2010 ncm membrane’ - Trusses were assumed Softwood timber @ 0.3m - Wooden battens instead of insulated wooden battens

Internal Floors - ‘T & G board’ assumed chipboard at 18mm - Ceiling Plasterboard used Ground Floor – Standard grade chipboard not flooring grade, flooring grade unavailable. Points to note on the reasoning for individual decisions are as follows: - All external and stairway doors have been included. - Flats that are being analysed include internal walls, internal doors and windows. - Flats that are not being analysed have all internal walls and windows but not doors, apart from front doors of flats on floor 3. - Stair and lift ‘holes’ have been modelled but individual steps have not. - Balconies have been modelled but do not include banisters (these are glass). 1 http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_wmo_region_6/country=GBR/cname=United%20Kingdom

Construction amended from default to match specifications are: Roof, external walls, internal walls, top floor ceiling, internal floors, ground floor, windows, doors, lighting, heating and water. 2 Airtightness The air tightness was set to 0.25 3 using the unit ac/h because research shows that this figure is a realistically achievable air tightness. The simulation program’s default was 0.05. ‘Natural Ventilation’ was set to ‘on’ because people inevitably open and close their windows. Cooling was turned off for the simulation because, for the purpose of this report, the building was assumed not to have air conditioning. The natural ventilation schedule has been left as default because it is impossible to estimate when occupants will choose to open their windows. I have only drawn doors in the areas off the studied area and the hallways off those areas. Services Lighting type has been set to ‘recessed’. The living space in each flat, which includes a lounge area, kitchen area and dining area, is 7.6 metres. The lighting in each flat is set to be on from 22:00 – 00:00 pm but only for the lounge area. As the simulation was set to the summer months, it assumes that natural lighting in the lounge area is sufficient outside these times. However, natural light can only travel approximately 6 metres so lighting would be needed in the back of the living space when occupied. This has not been taken into account in the simulation for overheating although, on reflection, it is likely to have an effect. The flats are heated by gas combi boilers and although heating is set to ‘off’ for the summer months the hot water is still needed. The schedule for hot water is set to the same as the occupancy schedule. The manufacturer states that the HVAC system is 91.1% efficient so the CoP in the simulation program has been changed to match. To improve simulation accuracy this system should be tested to assess if it is working at these levels of efficiency. To conclude, the DesignBuilder simulation program was used for its capacity to give estimates for energy performance. The model was amended to prioritise data of draught, solar gain, shading, heating and cooling, which will affect air temperature. 2 See appendix for designers specifications. 3 (http://ecoseal.knaufinsulation.us/documents/BI-WP-04_EcoSeal%20WP_air%20infiltration_full.pdf)

Results Tables Young professional (Occupied (Lounge) 18:00- 23:00, (Bedroom) 23:00- 8:00)

Flat number 118 119 121

Predicted Lounge Bedroom o o o 25 C 28 C 24 C 26oC > 5% >1% > 5% >1% 0% 0% 86% 0.17% 0% 0% 57% 0% 0% 0% 0% 0%

Actual* Lounge Bedroom o o o 25 C 28 C 24 C 26oC > 5% >1% > 5% >1% 40% 2.1% 9.7% 1.9% 84% 20% 93.6% 33.6% 20% 0.46% 24% 0%

Elderly or un-well occupant (Occupied (Lounge 8:00- 23:00. (Bedroom) 23:00- 8:00) Predicted Flat number 118 119 121

Lounge 25oC 28oC > 5% >1% 0% 0% 0% 0% 0% 0%

Actual*

Bedroom 24oC 26oC > 5% >1% 86% 0.17% 57% 0% 0% 0%

Lounge 25oC 28oC > 5% >1% 37% 1.7% 85% 23% 13% 0.3%

Bedroom 24oC 26oC > 5% >1% 9.7% 1.9% 93.6% 33.6% 24% 0%

*Actual Data and Predicted data is assessed on the hours of occupation only.

Total amount of overheating occurring in the flats in reality Actual Lounge Flat number 118 119 121

25 C > 5% 29% 75% 10%

Bedroom o

28 C >1% 1.3% 20% 0.2%

24 C > 5% 26% 96% 29%

26oC >1% 6% 46% 3.2%

Evaluation of Results The following analysis uses the CIBSE Guide A parameters to assess the effectiveness of DesignBuilder and the comfort of the occupants, in terms of air temperature. The table opposite shows the simulated and actual readings from the bedroom and lounge areas of flats 118, 119 and 121. The predicted data for each lounge area suggests that temperatures would consistently remain below the parameter for overheating. The figures from the predicted data were, on average, 22 oC 1 which is below the guidelines. However, the actual data shows that temperatures exceed the guidelines. Therefore, any confidence that the predicted figures offer the designer is unfounded. The bedroom data shows a difference between the flats. Flat 121 is located on the North side of the building, between two other flats. It performs the best initially in terms of overheating in both the predicted and actual results. The relationship between the bedrooms in 118, 119 and 121 is highlighted incorrectly on the predicted data, as flats 118 and 121 are equally comfortable. Flat 118 has a higher temperature in the lounge, whereas flat 121 has a higher temperature in the bedroom. Flat 119 appears to be a very ‘uncomfortable’ 2 flat, with elevated temperatures most of the occupied time. The bedroom in flat 118 actually performed better in real life, however all other results were worse. The results from the simulated model were inaccurate by a calculated factor of 1.6 3. The comparison between the experience of a young professional and an elderly or unwell person shows that overall the elderly occupant is more comfortable, apart from in flat 119, where the lounge is at a slightly higher temperature for the measured period of time. The third table has been included because the CIBSE Guide A regulations mean that overheating does not count in an unoccupied flat. Overall overheating statistics are important because contents depreciate quicker in higher heat and if occupants experience a change of circumstances, such as taking a prolonged period of time off work, they should not be uncomfortable in their home. These statistics show that the lounge areas in all flats overheat but less significantly than the bedrooms. This would create issues with flexibility of layout, for example if the occupants wished to change the use of this room to a home office. 1 To see the full set of results go to the appendix. 2 When using the term ‘uncomfortable’ the report is referring to standards that don’t meet the CIBSE guide A’s definition of comfort. 3 Using temperature data from living ro om and bedro om- not percentage over CIBSE guidelines.

Source: Module and overheating introduction lecture by Rory Jones.

Critical Review Variations in predicted and actual data can occur from three main groups of variables (see diagram opposite). The first to have an effect is the design. The design might be altered by accident when the model is redrawn in a modelling program. The design drawings might also be misinterpreted by the builders. Designs can also be flawed or incorrect from the beginning. The uncertainty within a simulation model is defined by Peter De Wilde as: • • • •

Specification uncertainty Scenario uncertainty Modelling uncertainty Numerical uncertainty 1

Specification uncertainty refers to whether the correct materials have been added. The credentials of that material should also be checked because the loaded data that the program stores may be inaccurate. With any product that derives from nature, for example bricks, there will be a degree of variation in specification from product to product. Manufacturers can be over optimistic about their product specifications. The simulation does not take into account the deterioration over time of systems. As seen in the section titled: ‘Building the model’ (page 18 & 19) there were many variations from specified materials to those that were input. Therefore some were inaccurate even before verifying specifications. Scenario uncertainty refers to the occupants (see page 26). There have been attempts to provide some reference to occupant behaviour in simulation programs but this has proved most difficult to simulate. Physiology, psychology and behavioural studies can be analysed, although it would be impossible to input these findings into a building simulation program accurately. Modelling uncertainty in this case refers to the fact that there is no weather data for this part of Devon. The closest is Mount Batten as Plymouth incurs a cost, so Jersey which has free data was used instead. Jersey is a small island in the English channel and weather conditions may vary from those at the development site. However, Torquay is slightly warmer than Jersey all year round but has slightly more frost, less sunshine and less rain. 2 The differences between these factors were not as great as expected but could still impact on the results. The weather data is from 2002 so is 13 years out of date, which could prove more of an issue than the substitution of the location. 1 2

Building Performance Simulation Lecture by Prof Peter de Wilde 2014 See appendix

External heat gain: 1, sun shines in, 2 objects emit the heat into the room. Source: Module and overheating introduction lecture by Rory Jones.

Internal heat gain: 1, solar panel, 2, hot pipes under the floor, 3, Boiler emitting heat into ro oms Source: Module and overheating introduction lecture by Rory Jones.

Numerical uncertainties refers to numerical data in all of the above points. Robust data and sensitive data refers to how well, tested designs, materials or methods are. Robust materials usually have more data available about them and are therefore more predictable. Using robust systems would potentially minimise the error in all the areas of uncertainty listed above but would limit product development. What causes overheating? External heat gain is a factor in overheating. The sun shines in through the windows and the objects inside absorb the heat and emit it. Furnishing and flooring types also affect emittion rates. In the UK, when sun shines directly into a room it is unlikely the occupants will implement solar shading but they are likely to open the windows. DesignBuilder used weather data to estimate these external heat gains; however the data was based on a different location where the hours of sunshine are more than for the real location. In reality, therefore, solar gains are likely to be lower. Internal heat gain is also important to consider but more difficult to predict. The values are largely defined by occupant behaviour including the fact that people themselves also give off heat. Objects that generate heat include computers, lights and kitchen appliances, which is relevant in open plan flats. Computers were not included because no other furniture or furnishings were added, but on reflection it should have been included as it would generate heat. In reality the heat from the kitchen and bathrooms would also have an effect depending on frequency of use. Any activity that involves letting in fresh air, adding or taking moisture from the air, anything that produces heat will effect the internal temperature. Choice of clothing will also have an effect on comfort, with elderly, unwell or very young babies often wearing more clothing. Metabolism, health and fitness of the occupant are also variables that affect the results. 3 The service design in buildings also causes internal heat gains, even with insulated pipes. Communal areas can also be a source of extra heat, especially when services run in these areas. The details about the heating and water were entered into the simulation, assuming a worse case scenario that the hot water is on all the time the flat is occupied. The services in the communal areas were not tested. Working from home and the increasing level of technology in homes creates extra heat. The average computer produces four to six times more heat than the average human. 4 There are also more people than ever living in cities, with 82% of the UK population living in urban areas. 5 This is leading to urban heat islands 3 Investigation into Overheating in Homes by the Department of Communities & Local govn. 4 http://club.myce.com/f7/how-much-heat-does-computer-give-off-187993/ 5 http://data.worldbank.org/indicator/SPU . RB.TOTL.IN.ZS?order=wbapi_data_value_2014+wbapi_data_value+wbapi_data_value-last&sort=asc

The map shows the development to be on the outskirts of town. Source: Google Maps

Orientation Source: Module and overheating introduction lecture by Rory Jones.

but similar, less severe effects cause urban areas to be warmer than their rural counterparts. Building space is at a premium and because of this flats are the most likely form of residence in cities and are the worst effected by overheating. This type of heat is not taken into account by the simulation program and is difficult to predict. Being near a body of water cools the building which can also affect the results. If noise polution or security are issues this will affect occupant behaviour. The flats used in this study, however, are on the fourth floor so security is not an issue.The location is not a busy one either so noise is not an issue. Urban heat can be an issue in very dense urban areas but Torquay is relatively sparse. In addition the map opposite shows that the area is semi rural. Orientation has certainly had an impact on the flats. The predicted and actual data show the North facing flat to be the most comfortable, despite being between two other flats. Certificated buildings often do not perform as predicted. (See map overleaf) The graph shows approximately a third of certificated buildings fail to meet predictions and some even perform below the code baseline. This affects the validity of the certification process, the reputation of the design and build team and the satisfaction of the occupants. If the simulation proves inaccurate, this can cause problems post-completion. Planning conditions or building regulations could be missed and planned certification could be halted. This is an issue for designers because, if their client required the certification as part of their brief, they may request compensation. More fundamentally the occupants will not be in the environment they expected and paid for. The limited details entered into DesignBuilder for this study were deliberatly below those expected to have been produced by the developer. However, the results still correlated with the developerâ&#x20AC;&#x2122;s conclusion that the building would not overheat, suggesting that detail is not the main factor in accuracy.

Source: Peter De Wilde Lecture, Bridging the energy Performance Gap 2014

Conclusion This report uses the results from a simulation program to inform a discussion on the performance gap. Overheating and air tightness are defined by CIBSE Guide A and the relevant user groups have been outlined. An explanation is provided as to why overheating is important and relevant now, in terms of demographics, building legislation and climate change. Simulation program context was provided to highlight DesignBuilderâ&#x20AC;&#x2122;s strengths and weaknesses in comparison with other programs. Using simulation programs to represent reality and the lack of their use in postcompletion analysis, were the two main issues identified. The simulation results were analysed and worse case scenarios (elderly or unwell) had the most comfortable accommodation. Of the three flats assessed flat 119 proved to be the most uncomfortable. Specific uncertainties and variants were discussed to explain the performance gap. This section concluded that robust materials usually have more data available about them and therefore more predictable. Using robust systems would potentially minimise the error in all the areas of uncertainty but would limit product development. Variations identified focused on surroundings, design and occupant behaviour. In conclusion the report highlights that when using simulated models, detail is not the main factor in accuracy. Simulation programs are an important part of the industry, but their emphasis should not be exaggerated. The alternative is to utilise design principles and simple calculations wherever possible, only resorting to complex simulations if the project demands it, in which case it should be used in conjunction with other evidence.

References Chartered Institution of Building Services Engineers (CIBSE). Guide A, environmental design. 7th ed. London, UK: CIBSE 2006. [CIBSE Guide A]. Dasgupta, A., Prodromou, A. and Mumovic, D. (2012). Operational versus designed performance of low carbon schools in England: bridging a credibility gap. HVAC&R Research, 18(1-2), 37-50. Department for Communities and Local Government (DCLG). Investigation into overheating in homes: Literature Review. Department for Communities and Local Government 2012:1-122. Department for Communities and Local Government (DCLG). Investigation into overheating in homes: Literature Review. Department for Communities and Local Government 2012:122. 1- Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/760 4/2185850.pdf Eppel H, Lomas KJ. Comparison of alternative criteria for assessing overheating in buildings. BRE Support Contract Report No. 12. Leicester: Scho ol of the Built Environment, Leicester Polytechnic (De Montfort University), UK: 1992 Page 10 images : Knowledge is Beautiful by David McCandless. Published: William Collins (25 Sept. 2014) Yu, Z., Haghighat, F., Fung, B., Morofsky, E., Yoshino, H. (2011). A methodology for identifying and improving occupant behaviour in residential buildings. Energy, 36, 6596-6608.

Appendix Spread sheets showing raw data Weather data for Torquay and Jersey Designers specifications Boiler Spec