Wind speed prediction model based on DWT and Randon Forest

Abstract

Wind energy is one of the fastest power generation technologies in the power generation industry and one of the most cost-effective methods of generating electrical power. For system reliability, improving highly appropriate wind speed forecasting methods is desirable. The wavelet transform is a powerful mathematical technique that converts an analyzed signal into a time-frequency representation. This technique helps forecast non-stationary time series. The objective is to evaluate the performance of the "DWT-Random Forest" model in predicting wind speed through a comparative analysis of performance metrics (MSE, RMSE, and MAPE) with similar studies. Our motivation is rooted in the pressing need to improve wind forecasting methods to optimize renewable energy generation. The method involved implementing the model, which presented performance metrics: MSE of 0.0099, RMSE of 0.0996, and MAPE of 0.0779. However, comparative analysis with previous studies reveals that our model demonstrates competitive performance. The main result of this study is the finding that the "DWT-Random Forest" model exhibits a respectable performance in predicting wind speed, although there is room for improvement.