Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction

Karijadi, Irene and Mulyana, Ig. Jaka ORCID: https://orcid.org/0000-0003-4910-3409 (2020) Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction. Jurnal Teknik Industri, 22 (1). pp. 11-16. ISSN pISSN: 1411-2485, eISSN: 2087-7439

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Abstract

Improving the accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is a difficult task due to non-stationary and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition (EEMD) is employed to decompose original data into several Intrinsic Mode Functions (IMFs). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power. Numerical testing demonstrated that the proposed method could accurately predict the wind power in Belgium.

Item Type:	Article
Uncontrolled Keywords:	Data mining, time series, prediction, renewable, energy.
Subjects:	Engineering > Industrial Engineering
Divisions:	Journal Publication
Depositing User:	F.X. Hadi
Date Deposited:	20 Nov 2024 04:29
Last Modified:	18 Feb 2025 02:56
URI:	https://repositori.ukwms.ac.id/id/eprint/41170

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