Building thermal resistance-capacitance model identification andindoor temperature prediction based on genetic algorithm

In order to accurately predict the indoor temperature of buildings, this study adopts the lumped parameter method to construct a building thermal resistance-capacitance (RC) model. The corresponding differential equations are derived, and the model is identified using the genetic algorithm. The RC model’s test set indicates an average absolute error of temperature of 0.14 ℃ and a R2of 0.99. The prediction results of indoor temperature between the RC model and two black box models are compared, and the prediction results of the RC model are more accurate. This paper discusses the applicability conditions of gray box and black box models. It is recommended to select an appropriate modeling method based on the needs and data conditions, and considering the complexity, predictive accuracy, interpretability and other factors of the model.