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

Basem Elshafei, Atanas Popov and Donald Giddings

Energy, 2024, vol. 310, issue C

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.

Keywords: Gaussian process regression; Temporal data fusion; Wind resource assessment; Data pre-processing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029839

DOI: 10.1016/j.energy.2024.133208

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