Using measure-correlate-predict methodologies for offshore wind resources quantification in a Mediterranean island scenario

Abstract

The accurate quantification of long-term wind resources is crucial for the design and optimization of offshore wind farms. This study explored the impact of highresolution Light Detection and Ranging (LiDAR) wind data on wind resources quantification in the central Mediterranean region, focusing on the generation of predicted long-term datasets and on offshore wind energy production. By correlating long-term wind datasets against measured short-term LiDAR data during two separate yet concurrent timeframes, researchers can improve wind speed predictions leading to better informed wind farm planning decisions. Four different Measure-Correlate-Predict (MCP) methodologies available in the windPRO® V4.0 software suite were employed to assess MCP method performance in predicting wind speeds at four specific locations outside Malta’s territorial waters and at one onshore location, where the LiDAR unit itself was situated. The results demonstrated a strong correlation between the long-term data and measured wind speeds during the concurrent time frames. The findings support the use of the MCP methodology and commercially-available long-term offshore wind data for wind farm planning and optimization decisions, particularly in the central Mediterranean region.