Comparison of Measure-correlation-predict Algorithms in Offshore Wind Power Assessment with Multi-year Observation of Automatic Weather Stations
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Abstract
For offshore wind power assessment, eight kinds of Measure-Correlation-Predict (MCP) algorithms have been employed to compare mean wind speed, standard deviation of wind speed, mean power density and annual energy output of wind turbine in six numerical experiments with wind observation of four automatic weather stations in Pearl River Delta and reanalysis dataset MERRA from 2003 to 2010. The results show that Simple Orthogonal Regression (SOR), Reduced Major Axis regression (RMA), Quantile Regression (QR), Weibull Scale Fitting (WS) predict better mean wind speed. Besides, SOR, RMA and WS predict better standard deviation of wind speed. Joint Probability Distribution algorithm (JPD) predicts better energy output of wind turbine.
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