Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting

Chengze Li, Tomohiro Murata

研究成果: Conference contribution

抄録

The short term electric load forecasting which is generally from one hour to one week is one of the intelligent electric grid (smart grid), for control of stable load supply hour-to-hour or day-to-day. The difficulty of short time forecasting is that the trend of time series usually change, and the non-adaptive auto-regressive integrated moving average (ARIMA) could not fit accurately. To solve that problem, conventional adaptive ARIMA with constant forgetting factor that gives a larger weight to more recent train data for dealing with non-stationary change of stochastic disturbance. The forgetting factor governs the recursive least squares (RLS) algorithm. However, constant forgetting factor usually result in over-fitting that increases forecasting error. A new adaptive ARIMA is proposed in this paper to improve the accuracy with lazy learning algorithm to reduce over-fitting error.

本文言語English
ホスト出版物のタイトルProceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018
編集者Oscar Castillo, David Dagan Feng, A.M. Korsunsky, Craig Douglas, S. I. Ao
出版社Newswood Limited
ISBN(電子版)9789881404886
出版ステータスPublished - 2018
イベント2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, Hong Kong
継続期間: 2018 3 142018 3 16

出版物シリーズ

名前Lecture Notes in Engineering and Computer Science
2
ISSN(印刷版)2078-0958

Other

Other2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018
国/地域Hong Kong
CityHong Kong
Period18/3/1418/3/16

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)

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