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Overview of Electric Power Load Forecasting Research

Author(s)

Runyang Yu

Affiliation(s)

School of Electrical Engineering, Shanghai University of Electric Power, Electrical Engineering and Automation (Sino-Foreign Cooperation in Running Schools), Shanghai, China

Abstract

Electric power load forecasting is a crucial link in ensuring the safe, stable, and economical operation of power systems and achieving sustainable resource allocation. This paper reviews the research status and development trends of electric power load forecasting, classifying it into three categories based on time span: short-term, medium-term, and long-term, suitable for daily grid dispatch, monthly/quarterly operational planning, and long-term planning, respectively. Regarding influencing factors, external meteorological conditions and economic policies, as well as internal user behavior patterns and power system dynamic characteristics, all significantly impact load forecasting. Traditional statistical methods such as time series analysis, regression analysis, and exponential smoothing once dominated but are limited by model assumptions and their ability to handle nonlinear relationships. Machine learning methods like Support Vector Machines (SVM) and Random Forests (RF) overcome traditional limitations through feature interaction and data fusion capabilities. Deep learning methods such as Convolutional Neural Networks (CNN), Transformers, and Graph Neural Networks (GNN) further enable high-dimensional modeling of complex spatiotemporal patterns, driving prediction accuracy to new heights. Future research directions focus on cross-domain collaborative forecasting, breaking disciplinary barriers to build multimodal fusion networks, and responding to carbon neutrality goals by developing net load forecasting models adapted to new power system demands and prediction models coupled with carbon credit mechanisms. In summary, power load forecasting technology is shifting from traditional approaches towards multidisciplinary integrated innovation and will become the core engine for intelligent decision-making in new power systems, aiding the safe, economical, and low-carbon transformation of the grid.

Keywords

Electric Power Load Forecasting; Meteorological Conditions; Machine Learning; Deep Learning; Carbon Neutrality

References

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