Short-term Load Forecasting of Multi-Energy in Integrated Energy System Based on Multivariate Phase Space Reconstruction and Support Vector Regression Mode

In order to alleviate the energy crisis and improve the energy utilization rate, the integrated energy system (IES) has become an important way of energy utilization. IES integrates electricity, natural gas, heating and cooling energy supply. Accurate energy load forecasting is essential, which has a significant impact on the economic scheduling and optimal operation of the IES. Herein, a combined model prediction method of multivariate phase space reconstruction (MPSR) and support vector regression (SVR) is proposed in this paper. First, a quantitative analysis of the coupling relationship between different integrated energy subsystems is conducted, and Pearson correlation analysis theory is used to analyse the historical time series of electrical, cooling, heat, gas loads and environmental factors one by one, then the input variables of the combined forecasting model are obtained. After that, the multivariate phase space is reconstructed by the C-C method, and the SVR model is used to predict electricity, cooling, heating and gas loads. Final, the model is validated by the actual data of the IES in Arizona State University, the results of three cases show the efficiency and high accuracy of the proposed forecasting method that considers the coupling relationship between multi-energy loads of IES.