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

Chengze Li, Tomohiro Murata

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018
EditorsOscar Castillo, David Dagan Feng, A.M. Korsunsky, Craig Douglas, S. I. Ao
PublisherNewswood Limited
ISBN (Electronic)9789881404886
Publication statusPublished - 2018 Jan 1
Event2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, Hong Kong
Duration: 2018 Mar 142018 Mar 16

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2
ISSN (Print)2078-0958

Other

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

Fingerprint

Electric load forecasting
Learning algorithms
Time series

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Li, C., & Murata, T. (2018). Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting. In O. Castillo, D. D. Feng, A. M. Korsunsky, C. Douglas, & S. I. Ao (Eds.), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018 (Lecture Notes in Engineering and Computer Science; Vol. 2). Newswood Limited.

Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting. / Li, Chengze; Murata, Tomohiro.

Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. ed. / Oscar Castillo; David Dagan Feng; A.M. Korsunsky; Craig Douglas; S. I. Ao. Newswood Limited, 2018. (Lecture Notes in Engineering and Computer Science; Vol. 2).

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

Li, C & Murata, T 2018, Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting. in O Castillo, DD Feng, AM Korsunsky, C Douglas & SI Ao (eds), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. Lecture Notes in Engineering and Computer Science, vol. 2, Newswood Limited, 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018, Hong Kong, Hong Kong, 18/3/14.
Li C, Murata T. Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting. In Castillo O, Feng DD, Korsunsky AM, Douglas C, Ao SI, editors, Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. Newswood Limited. 2018. (Lecture Notes in Engineering and Computer Science).
Li, Chengze ; Murata, Tomohiro. / Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting. Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. editor / Oscar Castillo ; David Dagan Feng ; A.M. Korsunsky ; Craig Douglas ; S. I. Ao. Newswood Limited, 2018. (Lecture Notes in Engineering and Computer Science).
@inproceedings{447ae0c134a94c8f8cca2637fcea28d4,
title = "Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting",
abstract = "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.",
author = "Chengze Li and Tomohiro Murata",
year = "2018",
month = "1",
day = "1",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
editor = "Oscar Castillo and Feng, {David Dagan} and A.M. Korsunsky and Craig Douglas and Ao, {S. I.}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018",

}

TY - GEN

T1 - Adaptive ARIMA model based on lazy learning algorithm for short period electric load forecasting

AU - Li, Chengze

AU - Murata, Tomohiro

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85062596844&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062596844&partnerID=8YFLogxK

M3 - Conference contribution

T3 - Lecture Notes in Engineering and Computer Science

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018

A2 - Castillo, Oscar

A2 - Feng, David Dagan

A2 - Korsunsky, A.M.

A2 - Douglas, Craig

A2 - Ao, S. I.

PB - Newswood Limited

ER -