Open AccessApplication of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction
Author(s) -
Irene Karijadi,
Ig. Jaka Mulyana
Publication year - 2021
Language(s) - English
Resource type - Journals
ISSN - 2087-7439
DOI - 10.9744/jti.22.1.11-16
Subject(s) - hilbert–huang transform , support vector machine , wind power , randomness , mode (computer interface) , wind power forecasting , ensemble forecasting , computer science , predictive modelling , volatility (finance) , power (physics) , regression , regression analysis , electric power system , data mining , artificial intelligence , mathematics , engineering , econometrics , machine learning , statistics , white noise , telecommunications , physics , quantum mechanics , electrical engineering , operating system
Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness 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 is employed to decompose original data into several Intrinsic Mode Functions (IMF). 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 can accurately predict the wind power in Belgian.