Research on multivariate flexible load prediction method for buildings based on multi-task balanced learning

Building flexible load scheduling is an effective means to alleviate the contradiction between supply and demand of the new power system, but it causes building loads to be more complex and variable, and the mutual coupling of air conditioning and power loads makes it more difficult to accurately predict building multivariate loads. In order to solve the above problems, this paper constructs a building multivariate load prediction model by using a cluster analysis method combined with time-of-use tariffs to identify the flexible energy use characteristics of the building energy system and optimizing the input parameters of the prediction model based on the temporal memory function of the long short term memory (LSTM) neural network and the coupled information sharing mechanism of the gradient-normalized multi-task learning (GNMTL) to improve the model’s multi-task balanced learning. An office building in Beijing is used as a case study to verify the effectiveness of the method. The results show that the proposed method can take into account the learning of the flexible characteristics and multivariate coupling relationship of building loads, with R2improved by 2.2% to 11.4% and RMSE and MAE reduced by 6.4% to 43.6% and 24.9% to 55.5%, respectively, compared with the existing methods. The research in this paper provides a more accurate load forecasting solution for building flexible load scheduling.