采用历史数据扩充方法的风力发电量月度预测

Monthly Forecast of Wind Power Generation Using Historical Data Expansion Method

月度风力发电量预测面临天气信息不确定性强和历史数据较少等问题,预测精度较低。根据风力所具有较强的季节特性和短时平滑变化特性,提出了风电月度发电量数据集扩展技术。基于扩展后的数据,利用中期天气预测信息前准后差的特点,提出了基于kdtree单元匹配、数据扩充、时间序列3种预测算法的熵权组合综合预测方法,通过理论及算例分析验证了所提数据扩充方法的有效性和综合预测方法的准确性。所提出的数据扩充方法和综合预测方法可为中长期风力发电月度电量预测提供一种可行的解决思路。

The monthly forecast of wind power generation faces such problems as strong weather information uncertainty and less historical data,so the prediction accuracy is low.Based on the strong seasonal characteristics and the short-term smooth change characteristics of wind power,a technique for expanding historical data of monthly wind power generation was proposed.Based on the extended historical data and the characteristics of forward and backward difference of the medium-term weather forecast information,an integrated entropy weight combination forecasting method based on three prediction algorithms of unit matching,data expansion,and time series is proposed,The validity of the proposed data augmentation method and the accuracy of the comprehensive prediction method are verified by theoretical and example analysis.The proposed data expansion method and comprehensive forecasting method can provide a feasible solution for the monthly power forecast of medium and long-term wind power generation.