Accuracy assessment of offshore wind measurements partially compensated by MCP method considering data availability

Abstract In Japan, most offshore promotion areas for bottom-fixed wind turbines are in near-shore areas, where offshore wind speeds are not uniform. The dual-scanning LiDAR system (DSL), which is a remote-sensing technology, is widely used to measure the mean wind speed, wind direction, and turbulence for resource and site assessments. However, the availability of DSL data tends to be lower than the key performance indicators proposed in the Carbon Trust in Offshore Wind Accelerator project owing to the characteristics of DSL. Missing observed data can be predicted using the measure–correlate–predict (MCP) method, and the uncertainty of the MCP method can be examined using a prediction function. However, a method to evaluate the accuracy of the final dataset comprising observed data and data predicted by the MCP method is yet to be devised. In this study, a set of formulas is proposed to evaluate the accuracy of indices, such as the coefficient of determination, slope, and offset of linear regression, for a partially complemented dataset using the MCP method. The proposed formulas are validated against on-site measurements.