Seasonal variation of residential cooling use behaviour derived from energy demand data and stochastic building energy simulation

Abstract

In recent years, a bottom-up approach based on building-energy simulations coupled with stochastic modelling of occupant behaviours has been intensively developed to properly estimate the effect of diverse and stochastic occupant behaviours on energy loads, mainly for developing smart building-energy-management systems and the promotion of renewable energy sources. Considering this background, the objective of this study is to elucidate the modelling of the state transitional probability of occupants’ heat pump (HP) use embedded in most bottom-up approaches based on statistical analysis of the 2-year electricity data of 586 dwellings. The analysis results clearly suggested that the relationship between daily hours of HP use and the daily mean outdoor air temperature has seasonal differences, in which frequency of HP use in early summer and early winter is lower than that in late summer and late winter under the same daily mean outdoor air temperature conditions. In addition, the authors presented statistical models of daily HP use hours with an explanatory variable of outdoor temperature for a lapse of 10 days to model the seasonal influence, and showed better performance compared to the model expressed by daily mean outdoor temperature. Furthermore, the authors revealed that such a seasonal change in behaviour is attributed to the state transitional probability of cooling use as a function of indoor thermal conditions based on a numerical simulation of the bottom-up approach. Finally, the authors proposed a revised model with the state transitional probability considering seasonal behaviour change, and the estimation results of household cooling use are consistent with the measured data.