Offshore wind power forecasting with wind-regime clustering and multi-scale feature learning

Short-term forecasting of offshore wind power is critical for grid stability, yet it remains a persistent challenge due to the inherent volatility and noise in both meteorological and power data. To address this challenge, a deep learning approach is introduced that integrates a wind-regime clustering strategy with an architecture combining an Inception network, a Bidirectional Long Short-Term Memory network, and a Multi-Head Self-Attention mechanism. Wind-direction-based pre-classification and K-means clustering are employed to organize historical data into distinct meteorological regimes, which are subsequently processed by the predictive model. The Inception network extracts multi-scale temporal features,…