A Short-Term Load Forecasting Model Based on Improved Random Forest Algorithm

机译：基于改进随机林算法的短期负荷预测模型

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Aiming at the current situation of local optimization and over-fitting in current load forecasting algorithms, this paper proposes an improved power system load forecasting model based on random forest algorithm. Considering that the power system load has the characteristics of schedule periodicity and temperature correlation, this paper uses the same cluster of data obtained by fuzzy clustering as the input set to construct the decision tree when establishing the random forest. Considering that the random forest algorithm has the disadvantage of over-fitting, this paper uses rough set theory to generate compensation rules to modify the prediction results of random forest. Finally, the load forecasting model described in this article was used to predict the electricity load of a certain area on January 1st, 2020, and compared with the existing forecasting model. The results of calculation examples show that the method described in this paper has better performance.

机译：旨在目前局部优化和过度拟合在电流负荷预测算法中，本文提出了一种基于随机林算法的电力系统负荷预测模型。考虑到电力系统负载具有调度周期性和温度相关的特性，本文使用通过模糊聚类获得的相同数据集群作为在建立随机林时构建决策树的输入设置。考虑到随机森林算法具有过度拟合的缺点，本文使用粗糙集理论来生成补偿规则来修改随机林的预测结果。最后，本文中描述的负载预测模型用于预测1月1日，2020年1月1日的特定区域的电力负荷，与现有的预测模型相比。计算实施例的结果表明本文中描述的方法具有更好的性能。

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《International Forum on Electrical Engineering and Automation》|2020年|928-931|共4页
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Huang Yiling; Huang Shaofeng;
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Load forecasting; Clustering algorithms; Rough sets; Predictive models; Prediction algorithms; Random forests; Load modeling;

机译：负载预测;聚类算法;粗糙集;预测模型;预测算法;随机森林;负载建模;

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A Short-Term Load Forecasting Model Based on Improved Random Forest Algorithm

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