 Machine learning application for estimating electricity demand by municipalityYoshiki Kusumoto, Rémi Delage and Toshihiko Nakata Energy, 2024, vol. 296, issue C 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. Keywords: Electricity demand; Demand sectors; Machine learning; Multilayer perceptron (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009113 DOI: 10.1016/j.energy.2024.131138 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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