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Short-term electricity load forecasting—A systematic approach from system level to secondary substations

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  • Pinheiro, Marco G.
  • Madeira, Sara C.
  • Francisco, Alexandre P.

Abstract

Energy forecasting covers a wide range of prediction problems in the utility industry, such as forecasting demand, generation, price, and power load over time horizons and different power levels. Short-term load forecasting allows the system operator to make important decisions during network management and planning, which represents an economic improvement in the global electrical system, as well as acts as a fundamental component to address challenges imposed by the energy transition. Despite the urgent need, research into the current state-of-the-art in low voltage forecasting, other than at the smart meters level, has not yet been carried out in an in-depth way. We propose a systematic approach from system level to low voltage considering not only the performance of the models but also the applicability, interpretability, and reproducibility of the method/model. An initial benchmark model was compared against improved regression models enhanced by introducing new synthetic explanatory variables and a different regression technique, generalized additive models. The error was reduced by 42%–47% compared to the benchmark model, preserving the interpretability. Additionally, a simple ensemble method was evaluated to determine how it improves accuracy for specific periods, such as weekends, summer vacation, or public holidays, in which modeling is particularly difficult using standalone generalized additive models. The method was applied to the national power load and, for the first time, to all 100,000 secondary substations that integrate the Portugal power grid, rather than to tackle the few open datasets in this area. The approach was used to build a daily forecasting system live called PREDIS (Portuguese acronym for Distributed Prediction) whose outcomes are used to anticipate load peaks and network constraints in the context of the Portugal distribution system operator.

Suggested Citation

  • Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
  • Handle: RePEc:eee:appene:v:332:y:2023:i:c:s0306261922017500
    DOI: 10.1016/j.apenergy.2022.120493
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    6. 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.

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