A novel spatio-temporal wind speed forecasting method based on the microscale meteorological model and a hybrid deep learning model

Abstract

Wind power, as a renewable alternative to conventional fossil fuel energy sources, exhibits substantial variability owing to fluctuations in wind velocity. In light of this, the present study puts forward a novel approach for spatio-temporal wind speed forecasting. It enables the prediction of multi-step ahead wind speeds at any location within a given region utilizing data from a single anemometer. The hybrid deep learning model and microscale meteorological model are employed to carry out the temporal and spatial wind speed forecasting, respectively. Specifically, we implement an enhanced complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique for signal decomposition to improve time series prediction accuracy, followed by prediction of subsequences via the hybrid deep learning model. In addition, instantaneous wind speed is divided into mean and fluctuating components, and they are then predicted by using the wind speed ratios, wind direction variations and correlation relationships between the observation location and the prediction site, obtained by the microscale meteorological model. Finally, the accuracy and reliability of the proposed model are validated against observational data. This innovative approach generates accurate wind speed forecasting in both temporal and spatial domains.