The accurate prediction of building indoor overheating risk is critical in order to mitigate its possible consequences on occupant health and wellbeing. The Chartered Institution of Building Services Engineers issued Technical Memorandum 59 (TM59) with the aim of achieving consistency in the modelling processes followed for the prediction of overheating risk in new dwellings. However, as each tool’s prediction may depend on its inherent assumptions, an inter-model comparison procedure was used to assess whether the choice of building performance simulation tool influences the overheating assessment. The predictions of two popular tools, IES VE and EnergyPlus, were compared for nine variations of a naturally ventilated, purpose built, London flat archetype, modelled under the default algorithm options. EnergyPlus predicted a high overheating risk according to TM59 criteria in seven out of the nine model variants, contrary to the low risk of all the IES VE variants. Analysis of heat transfer processes revealed that wind-driven ventilation and surface convection algorithms were the main sources of the observed discrepancies. The choice of simulation tool could thus influence the overheating risk assessment in flats, while the observed discrepancies in the simulation of air and heat transfer could have implications on other modelling applications.
Practical application:Technical Memorandum 59 issued by the Chartered Institution of Building Services Engineers may be widely adopted within the industry to assist the prediction of overheating risk in new dwellings. This work suggests that the choice of building performance simulation tool can greatly influence the predicted overheating risk. Furthermore, the differences identified in the modelling of heat transfer processes could also impact other modelling applications. Following these results, the need for detailed empirical validation studies of naturally ventilated homes has been highlighted.
LomasKJPorrittSM. Overheating in buildings: lessons from research. Build Res Inform2017; 45: 1–18.
2.
IPCC. Climate Change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change, Geneva, Switzerland, 2015.
3.
ZCH. Overheating in homes, the big picture, Full Report. Zero Carbon Housing, June 2015.
4.
AndersonMCarmichaelCMurrayVet al.Defining indoor heat thresholds for health in the UK. Perspect Public Health2013; 133: 158–164.
5.
JohnsonHKovatsRSMcGregorGet al.The impact of the 2003 heat wave on daily mortality in England and Wales and the use of rapid weekly mortality estimates. Euro Surveill2005; 10: 168–171.
6.
FouilletAReyGLaurentFet al.Excess mortality related to the August 2003 heat wave in France. Int Arch Occup Environ Health2006; 80: 16–24.
LoweR. Technical options and strategies for decarbonizing UK housing. Build Res Inform2007; 35: 412–425.
13.
HMG. The Building Regulations 2010. Approved Document L1a. Conservation of fuel and power in new dwellings. 2013 edition incorporating 2016 amendments. London, 2016.
14.
HMG. The Building Regulations 2010. Approved Document L1b. Conservation of fuel and power in existing dwellings. 2010 edition incorporating 2010, 2011, 2013 and 2016 amendments. London, 2016.
15.
ShrubsoleCMacmillanADaviesMet al.100 Unintended consequences of policies to improve the energy efficiency of the UK housing stock. Indoor Built Environ2014; 23: 340–352.
16.
BRE. The governments standard assessment procedure for energy rating of dwellings (SAP 2012). Watford: Department of Energy and Climate Change (DECC), 2014.
17.
CIBSE. Design methodology for the assessment of overheating risk in homes, TM59: 2017. London: Chartered Institution of Building Services Engineers, London, 2017.
18.
CIBSE. Building performance modelling, AM11:2015. Chartered Institution of Building Services Engineers, London, 2015.
19.
CrawleyDBHandJWKummertMet al.Contrasting the capabilities of building energy performance simulation programs. Build Environ2008; 43: 661–673.
20.
Judkoff R and Neymark J. International Energy Agency Building Energy Simulation Test (BESTEST) and diagnostic method. Technical report, NREL, February 1995.
21.
Neymark J and Judkoff R. International energy agency building energy simulation test and diagnostic method for heating, ventilating, and air-conditioning equipment models (HVAC BESTEST) Volume 1: cases E100E200. Technical report, NREL, January 2002.
22.
Judkoff R and Neymark J. Model Validation and Testing: The Methodological Foundation of ASHRAE Standard 140. In: The ASHRAE 2006 Annual Meeting, Quebec City, 24–29 June 2009. Golden, CO: NREL.
23.
SchwartzYRaslanR. Variations in results of building energy simulation tools, and their impact on BREEAM and LEED ratings: a case study. Energy Build2013; 62: 350–359.
24.
RaslanRDaviesM. Results variability in accredited building energy performance compliance demonstration software in the UK: an inter-model comparative study. J Build Perform Simul2010; 3: 63–85.
25.
Raslan R. Performance based regulations: the viability of the modelling approach as a methodology for building energy compliance demonstration. PhD Thesis, University College London, London, UK, 2010.
26.
MirsadeghiMCstolaDBlockenBet al.Review of external convective heat transfer coefficient models in building energy simulation programs: implementation and uncertainty. Appl Thermal Eng2013; 56: 134–151.
27.
Mantesi E, Hopfe CJ, Glass J, et al. Investigating the impact of modelling uncertainty on the simulation of insulating concrete formwork for buildings. In: Building performance and optimization conference. International Building Performance Simulation and Optimization, Newcastle University, UK, 12–14 September 2016, https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/21972 (accessed 19 July 2018).
28.
MavrogianniADaviesMChalabiZet al.Space heating demand and heatwave vulnerability: London domestic stock. Build Res Inform2009; 37: 583–597.
29.
MavrogianniAWilkinsonPDaviesMet al.Building characteristics as determinants of propensity to high indoor summer temperatures in London dwellings. Build Environ2012; 55: 117–130.
StrachanPSvehlaKHeuslerIet al.Whole model empirical validation on a full-scale building. J Build Perform Simul2016; 9: 331–350.
33.
MateusNMPintoAGraaGCd. Validation of EnergyPlus thermal simulation of a double skin naturally and mechanically ventilated test cell. Energy Build2014; 75: 511–522.
34.
SymondsPTaylorJMavrogianniAet al.Overheating in English dwellings: comparing modelled and monitored large-scale datasets. Build Res Inform2017; 45: 195–208.
35.
NikpourMKandarMZMousaviE. Empirical validation of simulation software with experimental measurement of self shading room in term of heat gain. World Appl Sci J2013; 21: 1200–1206.
IES. IES VE, www.iesve.com (2017, accessed 22 May 2017).
38.
BasuRSametJM. Relation between elevated ambient temperature and mortality: a review of the epidemiologic evidence. Epidemiol Rev2002; 24: 190–190.
39.
WHO. Improving public health responses to extreme weather/heat-waves – EuroHEAT. Technical report, World Health Organization (WHO), 2009.
40.
HackerJNSaullesTPDMinsonAJet al.Embodied and operational carbon dioxide emissions from housing: a case study on the effects of thermal mass and climate change. Energy Build2008; 40: 375–384.
41.
TaylorJDaviesMMavrogianniAet al.The relative importance of input weather data for indoor overheating risk assessment in dwellings. Build Environ2014; 76: 81–91.
42.
MavrogianniAPathanAOikonomouEet al.Inhabitant actions and summer overheating risk in London dwellings. Build Res Inform2016; 45: 119–142.
43.
PorrittSMCropperPCShaoLet al.Ranking of interventions to reduce dwelling overheating during heat waves. Energy Build2012; 55: 16–27.
44.
GuptaRGreggM. Using UK climate change projections to adapt existing English homes for a warming climate. Build Environ2012; 55: 20–42.
45.
NicolFHumphreysM. Derivation of the adaptive equations for thermal comfort in free-running buildings in European standard EN15251. Build Environ2010; 45: 11–17.
46.
CIBSE. The limits of thermal comfort: avoiding overheating in European buildings, TM52: 2013. Chartered Institution of Building Services Engineers, London, 2013.
47.
CIBSE. Guide A, environmental design. Chartered Institute of Building Services Engineers, London, 2015.
48.
BIS. BS EN 15251: 2007: indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Technical report, BSI, July 2008.
49.
NicolJFHumphreysMA. Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build2002; 34: 563–572.
50.
McCartneyKJFergus NicolJ. Developing an adaptive control algorithm for Europe. Energy Build2002; 34: 623–635.
51.
BrotasLNicolF. Estimating overheating in European dwellings. Archit Sci Rev2017; 60: 180–191.
52.
RamosNMAlmeidaRMSimõesMLet al.Knowledge discovery of indoor environment patterns in mild climate countries based on data mining applied to in-situ measurements. Sustainable Cities Soc2017; 30: 37–48.
53.
KimJde DearRParkinsonTet al.Understanding patterns of adaptive comfort behaviour in the Sydney mixed-mode residential context. Energy Build2017; 141: 274–283.
54.
FabiVAndersenRVCorgnatiSet al.Occupants’ window opening behaviour: a literature review of factors influencing occupant behaviour and models. Build Environ2012; 58: 188–198.
55.
MeinkeAHawighorstMWagnerAet al.Comfort-related feedforward information: occupants choice of cooling strategy and perceived comfort. Build Res Inform2017; 45: 222–238.
56.
OikonomouEDaviesMMavrogianniAet al.Modelling the relative importance of the urban heat island and the thermal quality of dwellings for overheating in London. Build Environ2012; 57: 223–238.
57.
CIBSE. Design Summer Years for London – TM49:2015. Chartered Institution of Building Services Engineers, 2014.
58.
ColeyDA. Representing top-hung windows in thermal models. Int J Ventilation2008; 7: 151158–151158.
59.
WelchBL. The generalization of ‘Student’s’ problem when several different population variances are involved. Biometrika1947; 34: 28–35.
60.
MantesiEHopfeCJCookMJet al.The modelling gap: quantifying the discrepancy in the representation of thermal mass in building simulation. Build Environ2018; 131: 7498–7498.
DOE. Input Output Reference. U.S. Department of Energy, September 2016.
65.
Petrou G, Mavrogianni A, Symonds P, et al. What are the implications of building simulation algorithm choice on indoor overheating risk assessment? In: Building simulation and optimization 2018 conference, Cambridge, United Kingdom, 2018. Manuscript accepted for publication.
