Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques

Biswajit Biswal, Subhasish Deb*, Subir Datta, Taha Selim Ustun, Umit Cali*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

This review offers an in-depth examination of Deep Learning (DL) and Machine Learning (ML) techniques for smart grid load forecasting, emphasizing language precision, methodological rigor, and the exploration of novel contributions. The language used in this review is both technical and accessible, balancing complex concepts with clear explanations to cater to both specialists and general readers. It meticulously dissects contemporary DL models, including neural networks and ensemble methods, and evaluates their effectiveness through a detailed review of algorithms and frameworks. The methodology section systematically compares these techniques against traditional forecasting methods using performance metrics such as MAPE, RMSE, and MSE, ensuring a comprehensive assessment of their accuracy and scalability. A significant contribution of this review is its examination of real-world applications and case studies, which demonstrate how ML and DL techniques address practical challenges in energy management, such as grid stability and demand forecasting. Furthermore, the review introduces novel perspectives on the integration of probabilistic forecasting and ensemble methods, which offer innovative approaches for managing energy demand uncertainties. By identifying current limitations and proposing future research directions, this review not only advances the understanding of DL and ML applications in smart grids but also provides a foundation for future developments in this evolving field.

Original languageEnglish
Pages (from-to)3654-3670
Number of pages17
JournalEnergy Reports
Volume12
Early online date27 Sept 2024
DOIs
Publication statusPublished - 1 Dec 2024

Bibliographical note

© 2024 The Authors.

Keywords

  • Deep learning
  • Ensemble methods
  • Load forecasting
  • Machine learning
  • Smart energy management
  • Smart grid

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