ISSN 1000-1026
CN 32-1180/TP
  • ISSN 1000-1026
  • CN 32-1180/TP

Citation: YE Lin,CHEN Xiaoyu,JIN Jingxin,LI Jiachen,TENG Jingzhu.Measure-Correlate-Predict Assessment Method of Wind Energy Resource Considering Wind Power Density and Wind Direction[J].Automation of Electric Power Systems,2019,43(3):24-32. DOI: 10.7500/AEPS20180710007 copy

Measure-Correlate-Predict Assessment Method of Wind Energy Resource Considering Wind Power Density and Wind Direction

  • Received Date: July 10, 2018
    Accepted Date: November 21, 2018
    Available Online: December 19, 2018

  • Abstract:

        In the wind resources assessment methods of regional wind farms, the observation data of reference stations is not sufficiently utilized by traditional measure-correlate-predict (MCP) methods to establish the mapping relationship between the wind power density of reference stations and the target station. Therefore, the forecasting accuracy of the long-term wind energy resources at the target station is not high. Based on the traditional MCP combination theory, two different MCP models are established with support vector regression (SVR). In addition, the traditional MCP models considering the characteristics of wind speed and direction are taken as the benchmarks to verify the effectiveness and accuracy of the proposed models. Results show that the determination coefficient of the proposed MCP method for wind power density prediction of the target station is higher than 0.9, and the forecasting accuracy and feasibility are significantly improved in comparison with those of traditional MCP models. Therefore, the proposed model can be applied to assess the wind energy resources of regional wind farms.


  • Keywords:

    wind farm; regional wind energy resource assessment; wind power generation; measure-correlate-predict (MCP); support vector regression (SVR); wind power density


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