Enhanced offshore wind resource assessment using hybrid data fusion and numerical models

Elshafei, Basem; Popov, Atanas; Giddings, Donald

Abstract

Wind resource assessments are crucial for pre-construction planning of wind farms, especially offshore. This study proposes a novel hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Wavelet Transform (EWT) for enhanced wind speed forecasting. This secondary decomposition reduces forecasting complexity by processing high-frequency signals. A Bidirectional Long Short-Term Memory (BiLSTM) neural network optimized with the Grey Wolf Optimizer (GWO) is then employed for forecasting. The model's accuracy is evaluated using simulated wind speeds along the coast of Denmark, combined with lidar measurements through data fusion. This approach demonstrates significant improvements in prediction accuracy, highlighting its potential for offshore wind resource assessment.

Citation

Elshafei, B., Popov, A., & Giddings, D. (2024). Enhanced offshore wind resource assessment using hybrid data fusion and numerical models. Energy, 310, Article 133208. https://doi.org/10.1016/j.energy.2024.133208

• Journal Article Type: Article
• Acceptance Date: Sep 16, 2024
• Online Publication Date: Sep 25, 2024
• Publication Date: Nov 30, 2024
• Deposit Date: Sep 17, 2024
• Publicly Available Date: Sep 26, 2025
• Journal: Energy
• Print ISSN: 0360-5442
• Electronic ISSN: 1873-6785
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 310
• Article Number: 133208
• DOI: https://doi.org/10.1016/j.energy.2024.133208
• Keywords: Gaussian process regression; Temporal data fusion; Wind resource assessment; Data pre-processing
• Public URL: https://nottingham-repository.worktribe.com/output/39718241
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0360544224029839
• Additional Information: This article is maintained by: Elsevier; Article Title: Enhanced offshore wind resource assessment using hybrid data fusion and numerical models; Journal Title: Energy; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.energy.2024.133208; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier Ltd.