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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647226
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: 2019 Jun 232019 Jun 27

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
CountryItaly
CityMilan
Period19/6/2319/6/27

Keywords

  • Daily electricity demand curve
  • Factor analysis
  • Machine learning
  • Seasonal model
  • Sparse model

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Kaneko, N., Hayashi, Y., & Fujimoto, Y. (2019). Toward data-driven identification of essential factors causing seasonal change in daily electricity demand curves. In 2019 IEEE Milan PowerTech, PowerTech 2019 [8810996] (2019 IEEE Milan PowerTech, PowerTech 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PTC.2019.8810996