Offshore wind power prediction based on two-stage hybrid modeling

• Published in: Energy strategy reviews Vol. 54; p. 101468
• Main Authors: Liang, Jiajia, He, Xiaoxia, Xiao, Haoyi, Wu, Chuanju
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-07-2024; Elsevier
• Subjects: Attention mechanism; Correlation vector machine; Cuckoo algorithm; Gated recurrent unit; Offshore wind power; Quantile regression; Gated recurrent unit; Attention mechanism; Correlation vector machine; Offshore wind power; Quantile regression; Cuckoo algorithm
• ISSN: 2211-467X
• DOI: 10.1016/j.esr.2024.101468

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.
•Introducing a novel two-stage model to tackle climate and power measurement challenges•Enhancing wind speed prediction accuracy by integrating GRU, AM, and QR models•Using CS-optimized RVM model to establish wind speed-power output relationship for reliable predictions•Handling cut-in and rated wind speeds to improve prediction accuracy•Demonstrating method effectiveness and adaptability across diverse climate conditions