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

张程珂; 刘会灯; 朱渝宁; 贾 凡; 郭恒青; 张金良

doi:10.3969/j.issn.1009-1831.2023.06.008

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

更新时间：2026-03-05

- Ultra-short-term photovoltaic power prediction for random forests based on multiple feature analysis and extraction
- Vol. 25, Issue 6, (2023)
- 作者机构：
  
  1. 国网重庆市电力公司,重庆,400014
  2. 国网重庆市区供电公司,重庆,400015
  3. 华北电力大学,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.06.008
  CLC：
- Published：2023
- 稿件说明：
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张程珂, 刘会灯, 朱渝宁, et al. Ultra-short-term photovoltaic power prediction for random forests based on multiple feature analysis and extraction[J]. 2023, 25(6).
DOI：

张程珂, 刘会灯, 朱渝宁, et al. Ultra-short-term photovoltaic power prediction for random forests based on multiple feature analysis and extraction[J]. 2023, 25(6). DOI： 10.3969/j.issn.1009-1831.2023.06.008.

摘要

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。

Abstract

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 missing and duplicate values. Then

correlation analysis is performed on the influencing factors and factors with strong correlation are selected. Next

feature engineering is performed on the screened factors and the processed feature vector is used as input of the prediction 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 accuracy

which is of certain guidance for PV prediction work.

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