Offshore wind power prediction based on two-stage hybrid modeling

Accurate prediction of offshore wind power generation is essential for efficient power scheduling and grid integration. This study introduces an innovative hybrid forecasting approach to address variability in power output due to different climatic conditions and challenges in measuring offshore wind speeds. The approach consists of two primary phases. First, a gated recurrent unit neural network with an attentional mechanism and a quantile regression model forecast wind speeds between cut-in and rated wind speeds, capturing trends under distinct climatic conditions. Second, wind speeds at nine quantile points are used as inputs to a relevance vector machine model, optimized via a cuckoo search algorithm, to establish the relationship between wind speed and power output. An empirical evaluation on a European offshore wind power dataset validates the approach's effectiveness across various climatic conditions. The two-stage model demonstrates enhanced adaptability, offering more reliable power predictions than conventional methods. Results indicate this hybrid forecasting method is more accurate than traditional techniques, significantly improving offshore wind power prediction performance.