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 (R C) 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 R C allocation that minimizes the heat flux through building walls. A thermal R C model with three resistances and two capacitances was used as an example to search for the optimal building R C 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 R C 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 R C 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 R C allocation of thermal R C models. • An inverse PSO method for the optimal RC allocation is proposed. • Optimization shows fewer steps and less time cost. • An optimal RC allocation is obtained. • Heat flux is reduced by 17.3%, 34.9% and 44.3% than three traditional RC allocations. • The effect mechanism of RC allocation on heat flux is explored. [ABSTRACT FROM AUTHOR]