“Mapping the current and future risk of overheating in UK homes” First Year Progress Report London-Loughborough CDT By Argyris Oraiopoulos 1

Aims & Objectives Literature Review Methodology Progress up to date Program of future work Questions Overview 2

Aim The aim of this research project is to investigate the current and possible future risk of summertime overheating in the UK housing stock and produce a mapping tool that would be able to identify and predict the risks across the UK. Objectives 1.Critically review previous work on measuring and modelling with regards to overheating in dwellings both in UK and internationally. 2.Capitalise on previous work and investigate the possibility of explaining the variation of internal temperatures in dwellings with the aid of an empirical model. 3.Decide on the appropriate modelling technique given the high level of data that can be available about a house. Is it an empirical model or the use of dynamic thermal simulation a more reliable method? Aims & Objectives 3

Objectives 4.Determine how to model each home to produce a fast efficient way of predicting overheating risk. Is it a whole house approach or is it the individual critical rooms that better suit the purpose of the study? And what are the metrics that should be used to indicate the risk of overheating according to this? 5.Analyse the incidence of current and future heatwave effects on homes. Decide on appropriate form of future weather file. Propose “design heatwave” weather file or use design summer year? 6.Run the model for a trial area, with and without refurbishment, and test different results presentation methods. 7.Run the results for the UK, and establish route for making results widely available. 8.Analyse the results and produce thesis and papers. Aims & Objectives 4

Measuring Datasets include anything from a single house to a few hundreds Limited availability of extensive datasets has not allowed a UK representative study Methods range from temperatures loggers to GIS mapping approaches and test chambers Overheating criteria used vary (CIBSE, BSEN, PHPP) but also variations within overheating criteria (CIBSE) Modelling Datasets include anything from a single house to 10,000 dwellings of various forms and size Large diversity of future weather files (forms, scenarios, years) Variety of modelling software (IES, Energy Plus, ESP-r, TAS) Number of different overheating criteria Literature Review 5

Dataset: Part of the 4M project dataset (230 houses – Free floating) House Characteristics Structure related (House type, House age, Wall type, Loft insulation) Occupancy related (Household size, Age of oldest occupant, Tenure) Empirical Model T rm =(1-α) (T ed +α T ed-1 +α 2 T ed ) High correlation between daily mean internal and T rm across sample Intercept and Slope from best fit of daily mean internal against T rm for every house Investigate relationship between house characteristics and Intercept Gains to losses ratio (G/L) based on SAP 2009 calculation Investigate relationship between G/L and Intercept Apply method across sample for both living rooms and bedrooms Methodology 6

Dynamic Thermal Simulation Initially model small sub sample (statistically representative if possible) Validation of models by comparison with measured data Attempt to reproduce intercept and slope Sensitivity analysis to identify the form of intercept and slope Overheating Metric & Weather Data Considering existing overheating metrics but also alternatives. UKCP s, 2050s, 2080s (Prometheus, University of Exeter) Is Design Summer Year suitable? Is there a need for “Design Summer Heatwave”? Mapping Tool Need to consider larger dataset (Energy Follow Up Survey, EST Homes Analytics) Four step process (archetype based) Modelling with and without refurbishments and passive cooling measures Methodology 7

Empirical Model Progress up to date 8 Initial Analysis using House characteristics No correlations found Secondary analysis using Gains to losses ratio Initially on 20 semi- detached houses Identification of possible trend

Empirical Model Progress up to date 9 Gains Losses RatioInterceptSlope Gains Losses Ratio Pearson Correlation1.716 ** ** Sig. (2-tailed) N20 Intercept Pearson Correlation.716 ** ** Sig. (2-tailed).000 N20 Slope Pearson Correlation-.563 ** ** 1 Sig. (2-tailed) N20 **. Correlation is significant at the 0.01 level (2-tailed). Statistical analysis on 20 semi- detached houses indicates strong correlation between intercept and gains to losses ratio Scatterplot indicates that the relationship is not linear Possible non-linear model could fit better Statistical analysis needs to extend and include whole sample for both living rooms and bedrooms Time series model could also be considered

Literature review 20 th Century Measuring studies International Modelling studies Empirical model Extending analysis to whole sample, for both living rooms and bedrooms and daytime / night time schedules SAP 2009 Comparison of SAP overheating metric and measured temperatures Dynamic thermal simulation Design Builder training Validation of models Reproduction of intercept and slope Program of Future Work 10

Overheating metrics Selection of appropriate metric according to the desired outcome Weather data Analysis of UKCP09 files and evaluation of length and severity of any heatwave periods included Mapping tool Determine which technique provides results more accurately and efficiently Follow up on methodology and attempt the development of an overheating hazard map. Program of Future Work 11

Questions ? Thank you 12

Overheating Graphs Predicting Overheating 13

Overheating Graphs tanh (x) y = 12 + (tanh(x-24)*12 for 20 ≤ x ≤ 28 Predicting Overheating 14

Overheating Graphs Summary Mean values graphs for all threshold values Results – Predicting Overheating 15