Time Series Analysis and Forecasting of Wind Speed Data

Meftah Elsaraiti, Adel Merabet, Ahmed Al-Durra

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

5 Scopus citations

Abstract

This paper discusses the problem of predicting wind speed using the statistical model based on autoregressive integrated moving average (ARIMA). Historical wind speed data, representing the Chester region of Nova Scotia, Canada, from 2012 to 2017, was used to operate this model. The form structure is defined by the rows p, d, q, and the length of the data period retrospectively. The structure parameters, autoregressive and moving average, were determined by the partial auto-correlation function and auto-correlation function, respectively. The model forecasting accuracy is based on the root mean square error, the mean absolute percentage error and the mean absolute error.

Original languageBritish English
Title of host publication2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538645390
DOIs
StatePublished - Sep 2019
Event2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States
Duration: 29 Sep 20193 Oct 2019

Publication series

Name2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Country/TerritoryUnited States
CityBaltimore
Period29/09/193/10/19

Keywords

  • Autoregressive integrated moving average
  • energy resources
  • forecasting
  • wind energy
  • wind speed

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