Knowledge-Data Fusion Model for Multivariate Load Short-Term Forecasting of Integrated Energy System

The short-term forecasting of multiple loads is crucial for the optimization and scheduling of integrated energy system (IES). However, the load within the IES exhibits diversified and strongly coupled characteristics, which seriously affects the forecast accuracy. Moreover, only using deep learning forecasting methods cannot analyze the factors that affect the forecast results, which is not conducive to guiding the optimization and scheduling of comprehensive energy systems. Therefore, a multivariate load forecasting model based on knowledge-guided multi-task spatial-temporal synchronous graph convolutional network is proposed. Firstly, the user clusters are classified according to the energy-using characteristics of different buildings. Then, the domain knowledge base is built by combining the dimensionless trends of different groups and expert experience. At the same time, the input features are filtered based on the improved maximum information coefficient method to construct spatial-temporal graph data, forming a more refined and efficient input sample data. Finally, the knowledge-data fusion model for multivariate load forecasting is constructed to predict local fluctuations of the multivariate load series and reconstruct the load ratio. The IES data set of Arizona State University Tempe Campus is taken as a test case. The results show that the proposed method is interpretable, has higher forecast accuracy and has better generalization ability.