基于小波变换的随机森林模型风力发电预测方法

Random Forest Model of Wind Power Forecasting Based on Wavelet Transform

针对风力发电因随机性、复杂性和易受外界因素干扰的特点而很难做出准确的分析和预测,提出了一种基于小波变换的随机森林模型风力发电预测方法。该方法利用国外某风电场的历史发电数据和气象历史数据作为输入参数,建立WT-RF模型,并与单一的随机森林模型作对比。试验结果证明,该预测模型能有效减小误差,具有一定的参考价值。

Because of the characteristics of randomness,complexity and easy to be interfered by external factors,it is difficult to make accurate analysis and prediction of wind power generation.A wind power forecasting method based on wavelet transform and random forest model is proposed.This method uses the historical power generation data and meteorological historical data of a foreign wind farm as input parameters to establish a WT-RF model,which is compared with a single random forest model.The experimental results show that the prediction model proposed in this paper can effectively reduce the error and has certain reference value.