Short-term electricity load forecasting—A systematic approach from system level to secondary substations
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DOI: 10.1016/j.apenergy.2022.120493
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Cited by:
- John O’Donnell & Wencong Su, 2023. "Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System," Energies, MDPI, vol. 16(15), pages 1-21, July.
- Mustafa Saglam & Xiaojing Lv & Catalina Spataru & Omer Ali Karaman, 2024. "Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning," Energies, MDPI, vol. 17(4), pages 1-22, February.
- Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
- Bianca Magalhães & Pedro Bento & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2024. "Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection," Energies, MDPI, vol. 17(8), pages 1-21, April.
- John O’Donnell & Wencong Su, 2023. "A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities," Energies, MDPI, vol. 16(21), pages 1-23, October.
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Keywords
Load forecasting; System level; Secondary substations; Ensemble model; Generalized additive models; Interpretable models;All these keywords.
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