A Wavelet-based hybrid multi-step Wind Speed Forecasting model using LSTM and SVR

Abstract

Wind energy, one of the greatest progressing renewable energy sources, becomes more significant for sustainable development and environmental protection. Its intermittent nature makes accurate and reliable predictions very challenging. Currently, hybrid models are extensively employed for wind speed forecasting and have been established to perform superior to traditional single forecast models. Hence, in this paper, a hybrid multi-step wind speed forecasting framework that combines the features of Wavelet Transform (WT), Long Short Term Memory (LSTM), and Support Vector Regression (SVR) is proposed. The prediction accuracy of the model is enhanced by denoising the dataset using wavelet transforms, which decomposes the data into low and high-frequency sub-series. The low-frequency sub-series is forecasted using LSTM network, and the high-frequency sub-series using SVR. Each forecasting outcomes are summed up to get the final forecasting results. The simulation results reveal that the forecast accuracy has significantly improved for the proposed wavelet-based hybrid model.