摘要
为了应对大规模电动汽车充电站负荷的调度管理,提出一种基于Hadoop的模糊灰色GA-BP神经网络负荷预测模型。在云平台上,运用模糊聚类及灰色关联度分析选取相似日,将相似日负荷代入Map Reduce架构下的GA-BP神经网络预测模型进行学习,获得待测日的预测负荷。以城市辖区快换式充电站实测数据进行实验,实验结果证明,此方法在快换式充电站的负荷预测上兼具高效性与精确性。
A fuzzy gray GA-BP neural network load forecasting model based on Hadoop is proposed to deal with the load dispatch management of large-scale electric vehicle charging station. The fuzzy clustering and gray relational analysis are used in the cloud platform to select the similar days,and then the similar daily loads are brought into the GA-BP neural network prediction model under Map Reduce architecture for learning,so as to obtain the forecasting load of the testing day. The experiment was performed for the measured data of quick-change charging station in city area. The experimental results show this method both has efficiency and accuracy for the load forecasting of the quick-change charging station.
作者
刘晓悦
孙玉容
LIU Xiaoyue;SUN Yurong(School of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China)
出处
《现代电子技术》
北大核心
2018年第13期74-77,82,共5页
Modern Electronics Technique
基金
国家自然科学基金资助项目(51574102)
国家自然科学基金资助项目(51474086).
关键词
Hadoop架构
模糊聚类
灰色关联分析
负荷预测
BP神经网络
快换式电动汽车充电站
Hadoop architecture
fuzzy clustering
gray relational analysis
load forecasting
BP neural network
quick-changeelectric vehicle charging station
