機械学習を用いた文教施設の電力需要予測における最適モデルの構築

DOI
  • 西嶋 瑛世
    富山大学大学院 理工学教育部 知能情報工学専攻
  • 古澤 陽
    富山大学大学院 持続可能社会創成学環 社会DSプログラム
  • 海野 真穂
    富山大学 都市デザイン学部 都市・交通デザイン学科
  • 堀田 裕弘
    富山大学 学術研究部 都市デザイン学系

書誌事項

タイトル別名
  • Construction of Optimal Model for Predicting Electricity Demand of Educational Facilities using Machine Learning

抄録

Since the Paris Agreement in 2015, there has been growing interest in global warming around the world, and accelerated measures to global warming are required to realize a decarbonized society by declaring the goal of carbon neutrality. As one of the measures, Demand Response (DR) are being actively introduced and provided. DR is a system whereby electric power companies pay incentives through transactions to consumers who cooperate in saving electricity during peak periods of electricity demand, thereby reducing peak electricity demand. Electricity demand for individual buildings is easily affected by seasonal fluctuations, the presence or absence of events, and other factors, and the occurrence of electricity demand peaks tends to be irregular, so highly accurate electricity demand forecasting is needed. In this study, we focus on the educational facilities such as University campus. The objective of this study is to use machine learning to construct a highly accurate forecasting model for not only the steady electricity demand in daily life, but also the characteristic electricity demand during events.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390014569436697600
  • DOI
    10.24778/jjser.44.3_145
  • ISSN
    24330531
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
  • 抄録ライセンスフラグ
    使用不可

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