Loading...

A high-accuracy hybrid method for short-term wind power forecasting

Khazaei, S ; Sharif University of Technology | 2022

32 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.energy.2021.122020
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. 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. © 2021
  6. Keywords:
  7. Feature selection ; Outlier detection ; Wavelet transform ; Wind power forecasting ; Anomaly detection ; Data handling ; Electric utilities ; Genetic algorithms ; Multilayer neural networks ; Numerical methods ; Radial basis function networks ; Time series ; Wavelet decomposition ; Weather forecasting ; Wind ; Wind power ; Features selection ; High-accuracy ; Hybrid method ; Numerical weather prediction ; Short-term wind power forecasting ; Times series ; Wavelets transform ; Wind farm ; Feature extraction ; Accuracy assessment ; Forecasting method ; Numerical method ; Time series analysis ; Spain
  8. Source: Energy ; Volume 238 , 2022 ; 03605442 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0360544221022684