A Review of State-of-the-Art AI and Data-Driven Techniques for Load Forecasting

Jian Liu, Xiaotian He, Kangji Li and Wenping Xue ()
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Jian Liu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaotian He: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Kangji Li: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Wenping Xue: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2025, vol. 18, issue 16, 1-27

Abstract: With the gradual penetration of new energy generation/storage, accurate and reliable load forecasting (LF) plays an increasingly important role in different energy management applications (e.g., power resource allocation, peak demand response, energy supply and demand optimization). In recent years, data-driven and artificial intelligence (AI) technologies have received considerable attention in the field of LF. This study provides a comprehensive review on the existing advanced AI and data-driven techniques used for LF tasks. First, the reviewed studies are classified from the load’s spatial scale and forecasting time scale, and the research gap that this study aims to fill in the existing reviews is revealed. It was found that short-term forecasting dominates in the time scale (accounting for about 83.1%). Second, based on the summary of basic preprocessing methods, some advanced preprocessing methods are presented and analyzed. These advanced methods have greatly increased complexity compared with basic methods, while they can bring significant performance improvements such as adaptability and accuracy. Then, various LF models using the latest AI techniques, including deep learning, reinforcement learning, transfer learning, and ensemble learning, are reviewed and analyzed. These models are also summarized from several aspects, such as computational cost, interpretability, application scenarios, and so on. Finally, from the perspectives of data, techniques, and operations, a detailed discussion is given on some challenges and opportunities for LF.

Keywords: load forecasting; artificial intelligence; data-driven techniques; deep learning; ensemble learning; reinforcement learning; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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