Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Dima Alberg, Mark Last

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (Auto Regressive Integrated Moving Average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (Online Information Network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
EditorsSatoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
PublisherSpringer Verlag
Pages299-307
Number of pages9
ISBN (Print)9783319544298
DOIs
StatePublished - 1 Jan 2017
Event9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
Duration: 3 Apr 20175 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10192 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Country/TerritoryJapan
CityKanazawa
Period3/04/175/04/17

Keywords

  • ARIMA
  • Incremental learning
  • Internet of things
  • Online Information network
  • Short-term forecasting
  • Sliding window
  • Smart grid

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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