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Machine learning application for estimating electricity demand by municipality

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  • Kusumoto, Yoshiki
  • Delage, Rémi
  • Nakata, Toshihiko

Abstract

In order to achieve efficient and low carbon energy systems, attention has been focused on locally distributed energy systems, but a detailed and accurate supply–demand database is essential for simulating and optimizing such local systems. Demand data are generally not maintained at small scales, and it is common to estimate electricity demand by prorating global data from large energy carriers. However, the accuracy of the proration method is questionable. The present study explores the idea of using machine learning as an alternative to the proration method for downscaling electricity demand data based on a neural network model. The input data consist of four categories: weekday/holiday determination data, temperature data, periodic signal data, and demand sector configuration data. The model shows a satisfying generalization capacity for downscaling data from regional to prefectural and municipal scales while capturing local characteristics that are not accounted with the proration method.

Suggested Citation

  • Kusumoto, Yoshiki & Delage, Rémi & Nakata, Toshihiko, 2024. "Machine learning application for estimating electricity demand by municipality," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009113
    DOI: 10.1016/j.energy.2024.131138
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    References listed on IDEAS

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    1. Rémi Delage & Taichi Matsuoka & Toshihiko Nakata, 2021. "Spatial–Temporal Estimation and Analysis of Japan Onshore and Offshore Wind Energy Potential," Energies, MDPI, vol. 14(8), pages 1-12, April.
    2. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    3. Naoya Nagano & Rémi Delage & Toshihiko Nakata, 2021. "Optimal Design and Analysis of Sector-Coupled Energy System in Northeast Japan," Energies, MDPI, vol. 14(10), pages 1-26, May.
    4. Kialashaki, Arash & Reisel, John R., 2014. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States," Energy, Elsevier, vol. 76(C), pages 749-760.
    5. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    6. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    7. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
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