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

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 T-2 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-II) 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. (C) 2021 Published by Elsevier Ltd.

Automated summary

This article proposes a high-accuracy hybrid approach for short-term wind power forecasting using historical data of wind farm and Numerical Weather Prediction (NWP) data, including three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. The method involves outlier detection, decomposition of time series, feature selection, and prediction using Multilayer Perceptron (MLP) neural network, with evaluation showing very high accuracy when tested with data from the Sotavento wind farm in Spain.