Review of Deep Learning-Based Load Forecasting, Diagnosis and Identification Models for Power Systems

DOI

10.2991/978-94-6463-864-6_78How to use a DOI?

Keywords

Deep Learning; Load Forecasting; Load Diagnosis; Load Recognition Model

Abstract

With the development of smart grid and data analytics, deep learning has become a key tool for improving the accuracy of load forecasting in power systems. This paper provides comprehensively reviews of the research advancements in deep learning in the field of load forecasting, diagnosis and identification. Starting from the foundation of deep learning, this paper describes its application in power system load forecasting models and provides a comparative analysis of the classification principles, evaluation criteria and performance of different forecasting models. Strategies to improve the accuracy of forecasting models are further explored, and the anomaly detection methods and diagnostic techniques of load data are summarized to profiled the identification challenges faced. Finally, this paper analyzes the current research trends, points out the existing problems, and potential avenues for future research were delineated and prospectively analyzed. In addition, the paper discusses the integration of advanced deep learning architectures highlighting their respective advantages in capturing temporal and spatial patterns in load data. Through an in-depth synthesis of theoretical development and practical application, this review aims to provide valuable guidance for researchers and practitioners seeking to enhance power system efficiency and resilience using intelligent forecasting and diagnostic models.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhengmou Wang
PY  - 2025
DA  - 2025/10/23
TI  - Review of Deep Learning-Based Load Forecasting, Diagnosis and Identification Models for Power Systems
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 896
EP  - 911
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_78
DO  - 10.2991/978-94-6463-864-6_78
ID  - Wang2025
ER  -