Volume 62

Impacts of Climate Change on Hourly Electricity Load in China Jinning Lyu, Jinhui Ren, Wenying Chen

Abstract

Accurate forecasting of electricity load is crucial for the stable operation of power systems and research related to energy systems. Electricity load is influenced not only by long-term socioeconomic factors but also by weather conditions. This study developed a high-resolution hourly electricity load forecasting model for China, utilizing machine learning techniques that integrate meteorological and socioeconomic data. The model demonstrates high accuracy in forecasting. National electricity demand and peak load are projected to rise steadily through 2030 and 2060. The peak-to-off-peak gap is expected to widen, and seasonal fluctuations are expected to intensify. Electricity load shows a positive correlation with extreme temperature, highlighting the impact of heatwaves on demand. These findings provide a solid foundation for further research on power system transformation under carbon neutrality pathways.

Keywords Climate change, Electricity load forecasting, Machine learning