A multi-stage multivariable offshore wind speed assessment system incorporating statistical analysis,decomposition,feature selection,bias correction,and attention-based hybrid prediction

Abstract

Efficient and accurate analysis of wind resources is essential for offshore wind power assessment and energy system dispatch. Different from single-variable prediction, this work proposes a multi-stage multivariable offshore wind speed assessment system. Firstly, the main variable is decomposed into three terms using seasonal-trend decomposition with loess (STL). Six highly correlated variables are then selected from 13 wind speed factors using a combination of three methods. Prediction model is built using Bidirectional Long Short-Term Memory networks (BiLSTM) based on Multi-Head Self-Attention (MSA) mechanisms, wherein Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) are added. Sub-term prediction biases are corrected using Artificial Neural Networks (ANNs) optimized by the Dung Beetle Optimizer (DBO). Wind speed for four specific months representing the four seasons of an offshore wind farm is computed. Comparative experiments show that the proposed method achieves excellent performance with high prediction accuracy.