A Comprehensive Multivariate Wind Speed Forecasting Model Utilizing Deep Learning Neural Networks

Abstract

Wind speed is influenced by various factors, necessitating the consideration of interactions between multiple meteorological elements in multivariate wind speed prediction. This paper proposes a multivariate combinatorial model based on a deep learning neural network, which incorporates not only historical wind speed data but also relevant meteorological features. Initially, the feature extraction of meteorological features. related to wind speed is first performed using an autoencoder (AE) and singular value decomposition (SVD). Subsequently, the complementary ensemble empirical mode decomposition and wavelet transform (CEWT) method is utilized to mitigate noise in the wind speed series. Finally, this paper utilize a gated recurrent unit (GRU) deep learning neural network for predicting the wind speed series. The optimization of the GRU’s 4 hyperparameters is accomplished through the implementation of the improved gray wolf algorithm (IGWO). This paper evaluates and validates the predictive performance of the model using two datasets. The experimental results demonstrate that the proposed model outperforms other models across 4 key performance indicators.