Research on Demand Response Modeling of Central Air Conditioning Considering Time Delay Effects

With the continuous development of energy management, demand response optimization of central air conditioning systems has become a key research topic, but the existence of time delay effects limits the accuracy and real-time performance of traditional models. This article proposes a modeling method based on an improved Elman neural network, which combines BP neural network and feedback neural network to construct a parallel model for predicting state parameters and time delay values. The research results show that the model performs well in predicting the time delay of water supply temperature and air supply temperature, and the predicted results are highly consistent with the actual values, which have been effectively validated in the cooling and heating seasons.