A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection

Oveis Abedinia; Ali Ghasemi-Marzbali; Mohammad Shafiei; Behrouz Sobhani; Gevork B. Gharehpetian; Mehdi Bagheri

doi:10.1109/EEEIC/ICPSEurope54979.2022.9854519

A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection

Oveis Abedinia
, Ali Ghasemi-Marzbali
, Mohammad Shafiei
, Behrouz Sobhani
, Gevork B. Gharehpetian
, Mehdi Bagheri

Nazarbayev University
Mazandaran University of Science and Technology
University of Mohaghegh Ardebili
Amirkabir University of Technology

Результат исследований

26 Цитирования (Scopus)

Аннотация

Wind energy is one of the most important resources for clean power generation. However, due to its periodic and irregular nature, prediction of its output power is very challenging for power system operation and planning. Therefore, in this work, a multi-level model for its power generation is proposed. First, wind speed is considered as an input signal with nonlinear behavior through multi-level model based on support vector machine (SVM), wavelet transform (WT) and entropy-based feature selection (FS). In this model, the wind signal is applied to the wavelet transform and after decomposition, will be considered as the input of feature selection. Finally, the proposed SVM is used to predict the best pattern. The proposed method is evaluated on real world engineering test case; the results verifies the accuracy and shows high speed of suggested method.

Язык оригинала	English
Название основной публикации	2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022
Издатель	Institute of Electrical and Electronics Engineers Inc.
ISBN (электронное издание)	9781665485371
DOI	https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854519
Состояние	Published - 2022
Событие	2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022 - Prague Продолжительность: июн. 28 2022 → июл. 1 2022

Серия публикаций

Название	2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022

Conference

Conference	2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022
Страна/Tерритория	Czech Republic
Город	Prague
Период	6/28/22 → 7/1/22

ЦУР ООН

Работа этого автора способствует достижению следующих Целей устойчивого развития

Affordable and clean energy

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality
Environmental Engineering

Доступ к документу

10.1109/EEEIC/ICPSEurope54979.2022.9854519

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Цитировать

Abedinia, O., Ghasemi-Marzbali, A., Shafiei, M., Sobhani, B., Gharehpetian, G. B., & Bagheri, M. (2022). A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection. В 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022 (2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854519

A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection. / Abedinia, Oveis; Ghasemi-Marzbali, Ali; Shafiei, Mohammad и др.
2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022).

Результат исследований

Abedinia, O, Ghasemi-Marzbali, A, Shafiei, M, Sobhani, B, Gharehpetian, GB & Bagheri, M 2022, A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection. в 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022, Prague, Czech Republic, 6/28/22. https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854519

Abedinia O, Ghasemi-Marzbali A, Shafiei M, Sobhani B, Gharehpetian GB, Bagheri M. A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection. В 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022). doi: 10.1109/EEEIC/ICPSEurope54979.2022.9854519

Abedinia, Oveis ; Ghasemi-Marzbali, Ali ; Shafiei, Mohammad и др. / A multi-level model for hybrid short term wind forecasting based on SVM, wavelet transform and feature selection. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022).

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AU - Sobhani, Behrouz

AU - Gharehpetian, Gevork B.

AU - Bagheri, Mehdi

N1 - Funding Information: ACKNOWLEDGMENT The authors acknowledge the financial support of this study by the Collaborative Research Project (CRP) Grant of Nazarbayev University under grant no. (021220CRP0322). Publisher Copyright: © 2022 IEEE.

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Y1 - 2022

N2 - Wind energy is one of the most important resources for clean power generation. However, due to its periodic and irregular nature, prediction of its output power is very challenging for power system operation and planning. Therefore, in this work, a multi-level model for its power generation is proposed. First, wind speed is considered as an input signal with nonlinear behavior through multi-level model based on support vector machine (SVM), wavelet transform (WT) and entropy-based feature selection (FS). In this model, the wind signal is applied to the wavelet transform and after decomposition, will be considered as the input of feature selection. Finally, the proposed SVM is used to predict the best pattern. The proposed method is evaluated on real world engineering test case; the results verifies the accuracy and shows high speed of suggested method.

AB - Wind energy is one of the most important resources for clean power generation. However, due to its periodic and irregular nature, prediction of its output power is very challenging for power system operation and planning. Therefore, in this work, a multi-level model for its power generation is proposed. First, wind speed is considered as an input signal with nonlinear behavior through multi-level model based on support vector machine (SVM), wavelet transform (WT) and entropy-based feature selection (FS). In this model, the wind signal is applied to the wavelet transform and after decomposition, will be considered as the input of feature selection. Finally, the proposed SVM is used to predict the best pattern. The proposed method is evaluated on real world engineering test case; the results verifies the accuracy and shows high speed of suggested method.

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KW - Support vector machine

KW - Wavelet transform

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