电测与仪表2017，Vol.54Issue(23)：41-46,6.
基于模糊聚类与随机森林的短期负荷预测
Short-time load forecasting based on fuzzy clustering and random forest
摘要
Abstract
Typical data mining methods (ANN and SVM) are applied in short-term load forecasting widely.However,these methods have some deficiencies including being trapped in local optimization easily and ensure the model hardly and so on.In order to overcome shortcomings,a method of combination of fuzzy clustering and random forest (RF) for load forecasting is proposed in this paper.On the other hand,various features of the periodical load and the similarity of input samples are considered in the proposed method.Input samples are clustered depending on similarity.Then,load forecasting model is established based on random forest algorithm and similar data are selected as training samples.The final results rely on the historical loads in Anhui for hourly load forecasting.And the results show that the proposed method is better than traditional support vector machine and BP neural network.关键词
模糊聚类/随机森林/数据挖掘/短期负荷预测Key words
fuzzy clustering/random forest/data mining/short-term load forecasting分类
信息技术与安全科学引用本文复制引用
黄青平,李玉娇,刘松,刘鹏..基于模糊聚类与随机森林的短期负荷预测[J].电测与仪表,2017,54(23):41-46,6.基金项目
中央高校基本科研业务费专项资金资助项目(2016MS09) （2016MS09）
