Abstract
Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a comprehensive overview of peak demand forecasting methods. It systematically reviews 186 studies published since the 1950s, categorizing these methods into three stages based on their developmental timeline. Building on this, the study defines a unified framework for peak demand forecasting and offers an in-depth analysis linking these methods to the practical needs of power systems. Notably, it highlights the growing importance of machine learning-driven forecasting models in addressing the increasing complexity of modern energy environments. Furthermore, this study identifies key research gaps and points out emerging trends that hold potential for advancing innovation in this field.
Recommended Citation
S. Dai and F. Meng and H. Dai and Q. Wang and X. Chen and W. Bai and P. Shi and R. Allmendinger and Y. Zhang and J. Liu, "Machine Learning in Peak Demand Forecasting: Foundations, Trends, and Insights," Renewable and Sustainable Energy Reviews, vol. 227, article no. 116500, Elsevier, Feb 2026.
The definitive version is available at https://doi.org/10.1016/j.rser.2025.116500
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
Deep learning; Machine learning; Peak demand forecasting; Power system; Smart grid; Time-series analysis
International Standard Serial Number (ISSN)
1879-0690; 1364-0321
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Feb 2026
