 A high-accuracy hybrid method for short-term wind power forecastingSahra Khazaei, Mehdi Ehsan, Soodabeh Soleymani and Hosein Mohammadnezhad-Shourkaei Energy, 2022, vol. 238, issue PC Abstract: In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic clustering and T2 statistic is employed for outlier detection. Effective feature selection is also carried out with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGA- ӀӀ) and the Radial Basis Function (RBF) Neural network. The evaluation of the proposed method using the data of Sotavento wind farm located in Spain demonstrates the very high accuracy of the proposed approach. Keywords: Wind power forecasting; Numerical weather prediction; Wavelet transform; Feature selection; Outlier detection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022684 DOI: 10.1016/j.energy.2021.122020 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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