DMPR: A novel wind speed forecasting model based on optimized decomposition, multi-objective feature selection, and patch-based RNN

Abstract

As the global demand for sustainable energy continues to grow, accurate wind speed prediction becomes crucial for optimizing the allocation of wind energy resources and enhancing the efficiency of wind power generation. This paper presents a composite prediction model that integrates Optimized Decomposition, Multi-Objective Feature Selection (MOFS), Patch-Based Recurrent Neural Networks (RNNs), and Cross-Attention Mechanisms. Initially, the wind speed time series is decomposed using RIME-optimized Variational Mode Decomposition (VMD), and sample entropy of each component is calculated to reconstruct high, medium, and low-frequency components. Subsequently, these components are patched to serve as inputs to the RNN layers. The third step involves the incorporation of exogenous variables, where MOFS is employed for feature selection, followed by a decomposition and reconstruction through RIME-optimized VMD. This process applies Variate-wise Cross-Attention Mechanism on the high, medium, and low-frequency components of both endogenous and exogenous variables. Finally, the outputs from all processing steps are integrated through a normalization layer and a feed-forward layer to produce the final prediction results. Experimental results demonstrate that our proposed model, through meticulous preprocessing and advanced architectural design, significantly improves the accuracy of wind speed predictions.