Modeling and optimization method for building energy performance in the design stage

Abstract

Building energy consumption is responsible for 30%–40% of the total society's energy consumption. There is great energy-saving potential in the building design stage. Good building designs can substantially reduce energy consumption in the long term. This study introduces a data-driven, modeling and optimization method for building energy performance in the design stage. The method has four steps: building model establishment, dataset generation, surrogate modeling, and optimization. Six potential regression methods and five potential optimization methods are applied and compared in two cases; where the regression models are used to train surrogate models, and the optimization methods are used to find the optimal designs. One case uses a constantly operating AC system and the other case uses an intermittently operating AC system. The results show the same trends in both cases. RFR has the best accuracy in predicting the annual building energy consumption, with RMSE consistently below 0.41 GJ for varying subcases. AdaBoost has the poorest performance among the six regression methods. The results indicate that 2000 to 3500 subcases are enough for the optimization of one case. In the case with a constantly operating AC system, the combination of XGBoost and DE found the best design with minimal annual building energy consumption of 1060 GJ; In the case with an intermittently operating AC system, the combination of GB and PSO found the best design with minimal annual building energy consumption of 565 GJ. The combination of RFR and PSO failed to find the optimal design in a limited time for both cases.