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

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

23 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665485371
DOIs	https://doi.org/10.1109/EEEIC/ICPSEurope54979.2022.9854519
Publication status	Published - 2022
Event	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 Duration: Jun 28 2022 → Jul 1 2022

Publication series

Name	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
Country/Territory	Czech Republic
City	Prague
Period	6/28/22 → 7/1/22

Funding

ACKNOWLEDGMENT The authors acknowledge the financial support of this study by the Collaborative Research Project (CRP) Grant of Nazarbayev University under grant no. (021220CRP0322).

Keywords

Feature selection
Support vector machine
Wavelet transform
Wind forecast

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

UN SDGs

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

Access to Document

10.1109/EEEIC/ICPSEurope54979.2022.9854519

Cite this

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. In 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 et al.
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).

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

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. in 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. In 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 et al. / 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

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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.

KW - Feature selection

KW - Support vector machine

KW - Wavelet transform

KW - Wind forecast

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

Abstract

Publication series

Conference

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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