Machine learning-based wind speed time series analysis

Tahir Cetin Akinci; Oguzhan Topsakal; Andrew Wernerbach

doi:10.1109/GEC55014.2022.9986887

Machine learning-based wind speed time series analysis

Tahir Cetin Akinci
, Oguzhan Topsakal
, Andrew Wernerbach

University of California at Riverside
Florida Polytechnic University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

4 Atıf (Scopus)

Özet

Machine Learning-based forecasting analysis provides high-accuracy results in estimating renewable energy sources. Having an accurate forecast of wind energy is essential to manage storage resources due to seasonal and geographical differences. In addition, the challenges posed by the discontinuity and uncertainty of wind power require accurate forecasts for energy economists and data scientists. In this study, hourly average wind speed data covering the years 2019, 2020, and 2021 in California were used to perform a time series analysis and forecasting utilizing one of the AutoML tools, Fedot. In addition, RMSE, MAE, and MAPE results were evaluated in the analyzes performed. Estimation results are consistent with these statistical evaluations.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	IEEE Global Energy Conference, GEC 2022
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	391-394
Sayfa sayısı	4
ISBN (Elektronik)	9781665497510
DOI'lar	https://doi.org/10.1109/GEC55014.2022.9986887
Yayın durumu	Yayınlandı - 2022
Harici olarak yayınlandı	Evet
Etkinlik	2022 IEEE Global Energy Conference, GEC 2022 - Batman, Türkiye Süre: 26 Eki 2022 → 29 Eki 2022

Yayın serisi

Adı	IEEE Global Energy Conference, GEC 2022

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2022 IEEE Global Energy Conference, GEC 2022
Ülke/Bölge	Türkiye
Şehir	Batman
Periyot	26/10/22 → 29/10/22

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Publisher Copyright:
© 2022 IEEE.

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Belgeye Erişim

10.1109/GEC55014.2022.9986887

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

@inproceedings{61df7fea096a48ff931add4bb8b7c8a6,

title = "Machine learning-based wind speed time series analysis",

abstract = "Machine Learning-based forecasting analysis provides high-accuracy results in estimating renewable energy sources. Having an accurate forecast of wind energy is essential to manage storage resources due to seasonal and geographical differences. In addition, the challenges posed by the discontinuity and uncertainty of wind power require accurate forecasts for energy economists and data scientists. In this study, hourly average wind speed data covering the years 2019, 2020, and 2021 in California were used to perform a time series analysis and forecasting utilizing one of the AutoML tools, Fedot. In addition, RMSE, MAE, and MAPE results were evaluated in the analyzes performed. Estimation results are consistent with these statistical evaluations.",

keywords = "AutoML, deep learning, machine learning, time series analysis, wind prediction, wind speed",

author = "Akinci, \{Tahir Cetin\} and Oguzhan Topsakal and Andrew Wernerbach",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Global Energy Conference, GEC 2022 ; Conference date: 26-10-2022 Through 29-10-2022",

year = "2022",

doi = "10.1109/GEC55014.2022.9986887",

language = "English",

series = "IEEE Global Energy Conference, GEC 2022",

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

pages = "391--394",

booktitle = "IEEE Global Energy Conference, GEC 2022",

address = "United States",

}

Akinci, TC, Topsakal, O & Wernerbach, A 2022, Machine learning-based wind speed time series analysis. in IEEE Global Energy Conference, GEC 2022. IEEE Global Energy Conference, GEC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 391-394, 2022 IEEE Global Energy Conference, GEC 2022, Batman, Türkiye, 26/10/22. https://doi.org/10.1109/GEC55014.2022.9986887

Machine learning-based wind speed time series analysis. / Akinci, Tahir Cetin; Topsakal, Oguzhan; Wernerbach, Andrew.
IEEE Global Energy Conference, GEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 391-394 (IEEE Global Energy Conference, GEC 2022).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Machine learning-based wind speed time series analysis

AU - Akinci, Tahir Cetin

AU - Topsakal, Oguzhan

AU - Wernerbach, Andrew

PY - 2022

Y1 - 2022

N2 - Machine Learning-based forecasting analysis provides high-accuracy results in estimating renewable energy sources. Having an accurate forecast of wind energy is essential to manage storage resources due to seasonal and geographical differences. In addition, the challenges posed by the discontinuity and uncertainty of wind power require accurate forecasts for energy economists and data scientists. In this study, hourly average wind speed data covering the years 2019, 2020, and 2021 in California were used to perform a time series analysis and forecasting utilizing one of the AutoML tools, Fedot. In addition, RMSE, MAE, and MAPE results were evaluated in the analyzes performed. Estimation results are consistent with these statistical evaluations.

AB - Machine Learning-based forecasting analysis provides high-accuracy results in estimating renewable energy sources. Having an accurate forecast of wind energy is essential to manage storage resources due to seasonal and geographical differences. In addition, the challenges posed by the discontinuity and uncertainty of wind power require accurate forecasts for energy economists and data scientists. In this study, hourly average wind speed data covering the years 2019, 2020, and 2021 in California were used to perform a time series analysis and forecasting utilizing one of the AutoML tools, Fedot. In addition, RMSE, MAE, and MAPE results were evaluated in the analyzes performed. Estimation results are consistent with these statistical evaluations.

KW - AutoML

KW - deep learning

KW - machine learning

KW - time series analysis

KW - wind prediction

KW - wind speed

UR - https://www.scopus.com/pages/publications/85146491672

U2 - 10.1109/GEC55014.2022.9986887

DO - 10.1109/GEC55014.2022.9986887

M3 - Conference contribution

AN - SCOPUS:85146491672

T3 - IEEE Global Energy Conference, GEC 2022

SP - 391

EP - 394

BT - IEEE Global Energy Conference, GEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Global Energy Conference, GEC 2022

Y2 - 26 October 2022 through 29 October 2022

ER -

Machine learning-based wind speed time series analysis

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

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