中国电机工程学报2017，Vol.37Issue(17)：4966-4973,8.DOI:10.13334/j.0258-8013.pcsee.160747
基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法
A Multiple Distributed BP Neural Networks Approach for Short-term Load Forecasting Based on Hadoop Framework
苏学能 1刘天琪 1曹鸿谦 2焦慧明 1于亚光 2何川 1沈骥2
作者信息
- 1. 四川大学电气信息学院,四川省成都市610065
- 2. 国网信通产业集团北京中电普华信息技术有限公司,北京市海淀区100192
- 折叠
摘要
Abstract
With the development of smart grids,communication network and sensor technology,the scale of power data is growing exponentially and complexly which gradually forms the big data.Up to now,traditional load forecasting methods have been unable to satisfy the analyzing requirements of massive load data.As a result,this paper put forward a short-term load forecasting method by incorporating multi-distributed BP neural networks combined with Hadoop Framework.First,on the basis of decomposing positive transferring process of input signals and back propagation of error signals in the BP neural network respectively,the load forecasting model of distributed BP neural network based on Hadoop framework was investigated and then implemented.Second,in order to weaken over-fitting problem,an clustering approach based on gray correlation and shortest distance method was utilized for the original number and the member set of multiple distributed BP neural networks after introducing the concept of the multiple,and then the valid index which determines the effectiveness of advantages and disadvantages of clustering results in order to determine the total number of models was also defined.Analytical results show that the prediction precision of multiple parallel BP neural network method is higher than that of the traditional BP neural network.关键词
负荷预测/Hadoop架构/分布式计算/BP神经网络/灰色关联度Key words
load forecasting/Hadoop framework/distributed computing/BP neural network/gray relational degree分类
信息技术与安全科学引用本文复制引用
苏学能,刘天琪,曹鸿谦,焦慧明,于亚光,何川,沈骥..基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J].中国电机工程学报,2017,37(17):4966-4973,8.
