Short-term wind speed prediction based on feature extraction with Multi-task Lasso and Multilayer Perceptron

The vigorous development of wind energy will help to promoting the adjustment of energy structure, the control of atmospheric smog, and the transformation of economic development mode. However, the randomness and volatility of wind speed make it difficult to predict with high accuracy. High-precision short-term wind speed prediction is inseparable from effective variable selection. In order to select highly relevant variables for short-term wind speed prediction, Multi-Task Lasso is applied to extract features, so the change trend of wind speed can be expressed exactly, and the Multilayer Perceptron (MLP) is applied to predict based on the extracted features. Firstly, Multi-Task Lasso is used to establish a linear regression model, the leading periods and the original input variables are determined from the perspective of prediction accuracy; Secondly, according to the optimization results of Multi-Task Lasso, the optimal features that can best express the wind speed fluctuation is extracted from different influencing factors and different leading periods based on the weight; Finally, the extracted optimal feature set is used as the input variable of MLP to establish prediction model. The results show that the effect of feature selection is obvious, the prediction accuracy is improved by more than 17%, and the short-term wind speed prediction model has good prediction accuracy and good generalization ability. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

The vigorous development of wind energy is crucial for adjusting energy structure, controlling atmospheric smog, and transforming economic development mode. However, the random and volatile nature of wind speed makes accurate prediction challenging. In this study, the Multi-Task Lasso method is applied for variable selection to improve the accuracy of short-term wind speed prediction. The extracted features are then used by the Multilayer Perceptron for prediction modeling. Experimental results demonstrate the significant impact of feature selection on prediction accuracy, achieving an improvement of over 17%.