A novel short-term multi-energy load forecasting method for integrated energy system based on two-layer joint modal decomposition and dynamic optimal ensemble learning

Abstract

Accurate short-term multi-energy load forecasting is the cornerstone for optimal dispatch and stable operation of integrated energy system (IES). However, due to the complexity and coupling inside IES, multi-energy load forecasting faces serious challenges with data nonlinearity and instability, leading to reduced prediction accuracy. To this end, a novel short-term multi-energy load forecasting method for IES based on two-layer joint modal decomposition (TLJMD) and dynamic optimal ensemble (DOE) learning is developed in this paper. Firstly, the TLJMD method is proposed to decompose the nonlinear and nonstationary multi-energy load into several intrinsic mode functions (IMFs) to capture the periodicity and regularity within the multi-energy load. Secondly, the uniform information coefficient method is employed to select calendar, meteorological, and coupling feature that exhibit strong correlation with the multi-energy load. Eventually, the DOE model consisting of four base learners and the ensemble weight forecasting model is constructed, the IMFs and selected features are input into the DOE model to achieve the final forecasting results. The proposed method is tested on the publicly available data set from real-world scenario and compared with various forecasting methods to assess its effectiveness and accuracy. The simulation results indicate that the proposed method outperforms other forecasting methods in short-term multi-energy load forecasting for IES, with mean absolute percentage error values of 1.7025 %, 2.2244 %, and 2.3808 % for electric, heating, and cooling load forecasting, respectively.