Event effects estimation on electricity demand forecasting

Kei Hirose, Keigo Wada, Maiya Hori, Rin Ichiro Taniguchi

研究成果: ジャーナルへの寄稿学術誌査読

3 被引用数 (Scopus)


We consider the problem of short-term electricity demand forecasting in a small-scale area. Electric power usage depends heavily on irregular daily events. Event information must be incorporated into the forecasting model to obtain high forecast accuracy. The electricity fluctuation due to daily events is considered to be a basis function of time period in a regression model. We present several basis functions that extract the characteristics of the event effect. When the basis function cannot be specified, we employ the fused lasso for automatic construction of the basis function. With the fused lasso, some coefficients of neighboring time periods take exactly the same values, leading to stable basis function estimation and enhancement of interpretation. Our proposed method is applied to the electricity demand data of a research facility in Japan. The results show that our proposed model yields better forecast accuracy than a model that omits event information; our proposed method resulted in roughly 12% and 20% improvements in mean absolute percentage error and root mean squared error, respectively.

出版ステータス出版済み - 11月 1 2020

!!!All Science Journal Classification (ASJC) codes

  • 再生可能エネルギー、持続可能性、環境
  • 燃料技術
  • エネルギー工学および電力技術
  • エネルギー(その他)
  • 制御と最適化
  • 電子工学および電気工学


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