Time Series Analysis and Forecasting of Wind Speed Data

Meftah Elsaraiti; Adel Merabet; Ahmed Al-Durra

doi:10.1109/IAS.2019.8912392

Time Series Analysis and Forecasting of Wind Speed Data

Meftah Elsaraiti
, Adel Merabet
, Ahmed Al-Durra

Electrical Engineering

Saint Mary's University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 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 language	British English
Title of host publication	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538645390
DOIs	https://doi.org/10.1109/IAS.2019.8912392
State	Published - Sep 2019
Event	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States Duration: 29 Sep 2019 → 3 Oct 2019

Publication series

Name	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Country/Territory	United States
City	Baltimore
Period	29/09/19 → 3/10/19

Keywords

Autoregressive integrated moving average
energy resources
forecasting
wind energy
wind speed

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IAS.2019.8912392

Cite this

@inproceedings{80a9083f8d884dcfa98bbc53eebe1ab2,

title = "Time Series Analysis and Forecasting of Wind Speed Data",

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.",

keywords = "Autoregressive integrated moving average, energy resources, forecasting, wind energy, wind speed",

author = "Meftah Elsaraiti and Adel Merabet and Ahmed Al-Durra",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 ; Conference date: 29-09-2019 Through 03-10-2019",

year = "2019",

month = sep,

doi = "10.1109/IAS.2019.8912392",

language = "British English",

series = "2019 IEEE Industry Applications Society Annual Meeting, IAS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 IEEE Industry Applications Society Annual Meeting, IAS 2019",

address = "United States",

}

Elsaraiti, M, Merabet, A & Al-Durra, A 2019, Time Series Analysis and Forecasting of Wind Speed Data. in 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019., 8912392, 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Baltimore, United States, 29/09/19. https://doi.org/10.1109/IAS.2019.8912392

Time Series Analysis and Forecasting of Wind Speed Data. / Elsaraiti, Meftah; Merabet, Adel ; Al-Durra, Ahmed.
2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8912392 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Time Series Analysis and Forecasting of Wind Speed Data

AU - Elsaraiti, Meftah

AU - Merabet, Adel

AU - Al-Durra, Ahmed

PY - 2019/9

Y1 - 2019/9

N2 - 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.

AB - 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.

KW - Autoregressive integrated moving average

KW - energy resources

KW - forecasting

KW - wind energy

KW - wind speed

UR - https://www.scopus.com/pages/publications/85076732123

U2 - 10.1109/IAS.2019.8912392

DO - 10.1109/IAS.2019.8912392

M3 - Conference contribution

AN - SCOPUS:85076732123

T3 - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

BT - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Y2 - 29 September 2019 through 3 October 2019

ER -

Time Series Analysis and Forecasting of Wind Speed Data

Abstract

Publication series

Conference

Keywords

UN SDGs

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