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May 27, 2026Journal of Modern Power Systems and Clean EnergyOpen Access

A Review on Deep Learning-based Electrical Load Forecasting: From Perspectives of Learning Paradigms and Foundation Models

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Authors

PZPengfei ZhaoHWHu WeihaoCDCao Di

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Overview

Review highlights how deep learning models for electrical load forecasting evolve, suggesting new research opportunities.

Key Points

  • The review examines deep learning techniques and their learning paradigms for effective electrical load forecasting.
  • Survey the literature on deep learning-based electrical load forecasting.
  • Organize findings into four learning paradigms: task-tuned offline learning, adaptive DL, collaborative DL, general-purpose DL.
  • Analyze the integration of foundation models into the existing deep learning approaches.
  • Outlines four orthogonal paradigms for better understanding deep learning evolution in electrical load forecasting.
  • Identifies key challenges in current techniques and suggests necessary advancements.
  • Highlights the importance of adapting learning paradigms to improve forecasting accuracy.

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/6a16891d0c924ddd1bd57e9ahttps://doi.org/10.35833/mpce.2025.000740
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