Event effects estimation on electricity demand forecasting

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

Research output: Contribution to journalArticlepeer-review

3 Citations (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.

Original languageEnglish
Article number5839
Issue number21
Publication statusPublished - Nov 1 2020

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Energy (miscellaneous)
  • Engineering (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Building and Construction
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment


Dive into the research topics of 'Event effects estimation on electricity demand forecasting'. Together they form a unique fingerprint.

Cite this