Inverse optimization of building thermal resistance and capacitance for minimizing air conditioning loads

Aiming at reducing the heating and cooling loads for built environments, the thermal resistance and capacitance (RC) model has widely been acknowledged as an effective method for predicting the heat flux through building walls by simplifying wall structures and their properties into R and C allocations. This paper demonstrates an inverse optimization method based on particle swarm optimization (PSO) for determining the optimal RC allocation that minimizes the heat flux through building walls. A thermal RC model with three resistances and two capacitances was used as an example to search for the optimal building RC allocation in the hot summer and warm winter zone of China. The inverse optimization could be efficiently accomplished in 2.5 h with an ordinary computer, approximately 0.12% of the time consumed by using the exhaustive search method. The optimized RC allocation was composed of three resistances of 0.43, 0.18 and 0.39 and two capacitances of 0.50 and 0.50. Compared to the three typical thermal insulation (e.g., internal, external, and internal/external), the optimal RC allocation could reduce the heat flux into/from buildings by 17.3%–44.3%. The proposed inverse PSO method shows an effective and efficient capacity in searching for the optimal RC allocation of thermal RC models.