A panel model for predicting the diversity of internal temperatures from English dwellings
Using panel methods, a model for predicting daily mean internal temperature demand across a heterogeneous domestic building stock is developed.
The model offers an important link that connects building stock models to human behaviour. It represents the first time a panel model has been used to estimate the dynamics of internal temperature demand from the natural daily fluctuations of external temperature combined with important behavioural, socio-demographic and building efficiency variables. The model is able to predict internal temperatures across a heterogeneous building stock to within ~0.71°C at 95% confidence and explain 45% of the variance of internal temperature between dwellings.
The model confirms hypothesis from sociology and psychology that habitual behaviours are important drivers of home energy consumption. In addition, the model offers the possibility to quantify take-back (direct rebound effect) owing to increased internal temperatures from the installation of energy efficiency measures. The presence of thermostats or thermostatic radiator valves (TRV) are shown to reduce average internal temperatures, however, the use of an automatic timer is statistically insignificant.
The number of occupants, household income and occupant age are all important factors that explain a proportion of internal temperature demand. Households with children or retired occupants are shown to have higher average internal temperatures than households who do not. As expected, building typology, building age, roof insulation thickness, wall U-value and the proportion of double glazing all have positive and statistically significant effects on daily mean internal temperature. In summary, the model can be used as a tool to predict internal temperatures or for making statistical inferences.
However, its primary contribution offers the ability to calibrate existing building stock models to account for behaviour and socio-demographic effects making it possible to back-out more accurate predictions of domestic energy demand.
Kelly, S., M. Shipworth, D. Shipworth, M. Gentry, A. Wright, M. Pollitt, D. Crawford-Brown, and K. Lomas