Toward data-driven identification of essential factors causing seasonal change in daily electricity demand curves

研究成果: Conference contribution

抄録

The forecast of medium-/long-term electricity demand helps the system operators and energy suppliers to plan appropriate facilities and supply. In particular, information of daily electricity demand curve is necessary for several problems. Traditionally, a regression approach focusing on a few variables has been studied widely from the viewpoint of deriving possible scenarios. However, the prediction of demand had becoming difficult with these traditional frameworks, so that further detailed regression approaches considering seasonal factors among numerous variables have been studied. In this study, the authors propose an approach to identify the essential factors causing seasonal change in daily demand curve using seasonal models constructed based on machine learning techniques; in this scheme, the consistency of selected variables in seasonal models plays a key role for deriving interpretable results. This study introduces an approach to derive the minimal number of important variables for identification of essential factors causing seasonal change in demand.

本文言語English
ホスト出版物のタイトル2019 IEEE Milan PowerTech, PowerTech 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538647226
DOI
出版ステータスPublished - 2019 6
イベント2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
継続期間: 2019 6 232019 6 27

出版物シリーズ

名前2019 IEEE Milan PowerTech, PowerTech 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
国/地域Italy
CityMilan
Period19/6/2319/6/27

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • エネルギー工学および電力技術
  • 再生可能エネルギー、持続可能性、環境
  • 安全性、リスク、信頼性、品質管理

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