基于多特征分析提取的随机森林超短期光伏功率预测

Ultra-short-term photovoltaic power prediction for random forests based on multiple feature analysis and extraction

随着新能源的大规模利用,光伏渗透率稳步增长,准确的光伏功率预测能为电网企业带来较多效益。基于此提出了一种多特征分析提取的随机森林预测模型,用于超短期光伏功率预测。首先,对收集到的光伏数据进行预处理,清理缺失值和重复值;再次,对影响因素进行相关性分析,选取相关性强的因子;然后,对筛选后的因子进行输入特征量选择,将处理后的特征向量作为预测模型的输入;最后,建立随机森林预测模型,并与BP、RBF、MLP模型对比。实证结果表明,所提模型具有较好的拟合度和更高的预测精度,对光伏预测工作有一定的指导意义。

PV penetration is steadily increasing with the large-scale utilization of new energy sources.Accurate PV power prediction can bring more benefits to grid enterprises.Based on this,a random forest prediction model with multi-feature analysis extraction is proposed for ultra-short-term PV power prediction.Firstly,the collected PV data is pre-processed to clean up the miss-ing and duplicate values.Then,correlation analysis is performed on the influencing factors and factors with strong correlation are se-lected.Next,feature engineering is performed on the screened fac-tors and the processed feature vector is used as input of the predic-tion model.Finally,the random forest prediction model is built and compared with BP,RBF and MLP models.Empirical results show that the model proposed has better fit and higher prediction accura-cy,which is of certain guidance for PV prediction work.