Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression

Abstract

Balancing electricity supply and demand is crucial for China's energy transition and the stability of its electricity market. Accurate prediction of mid-term electricity demand plays a vital role in mitigating supply-demand imbalances, enabling policymakers and power plants to make informed decisions. This study proposes a novel hybrid model, EEMD-SE-PSO-SVR, to forecast mid-term electricity demand in China. The model integrates ensemble empirical mode decomposition (EEMD) with sample entropy (SE) for data preprocessing, particle swarm optimization (PSO) for parameter optimization, and support vector regression (SVR) for prediction. Our findings demonstrate that the EEMD-SE-PSO-SVR model outperforms traditional benchmark models, achieving a 54.49 % reduction in mean absolute percentage error (MAPE) compared to the SVR model. The model's performance is significantly enhanced by EEMD-SE, which effectively addresses the non-stationarity of electricity demand data. Moreover, the analysis highlights the strong influence of economic factors, followed by the seasonal factors and energy structure, underscoring their importance in accurately forecasting electricity demand. These findings contribute valuable insights for improving the accuracy of mid-term electricity demand forecasts and support the development of carbon-neutral and peak-carbon policies in China.