一种新的电力负荷预测分布式算法  

A New Power Load Forecasting Distributed Algorithm

作　　者：赵硕[1] 张少敏[1] 

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《小型微型计算机系统》2014年第9期2142-2146,共5页Journal of Chinese Computer Systems

基　　金：河北省科学研究项目(Z2012077;Z2010290)资助

摘　　要：面对电力系统智能化所带来的负荷数据海量化高维化的趋势,解决对海量高维电力负荷数据进行负荷预测时所面临的单机计算资源不足的问题,提出将云计算引入到海量数据的负荷预测领域,缓解单机计算资源不足的瓶颈.此外,从提高算法效率的角度,提出将序列极小优化（SMO）算法引入到电力负荷支持向量机预测算法（ε-SVR）中,实现对ε-SVR算法的快速训练;采用云计算的MapReduce编程框架以及HBase分布式数据库对算法进行并行化改进,设计相应的Map和Reduce函数实现并行化设计.选用EUNITE竞赛提供的真实电力负荷预测数据进行算例分析,在9节点云计算集群上进行实验,实验结果表明在改进后的并行负荷预测算法随着云集群节点数的增加,9节点集群训练阶段的速度比单机运行SVM算法快7~8倍,且预测准确率至少提高了5%.Facing the trend of high dimensional and huge amounts of data caused by smart power system promotion, in order to solve the problem of insufficient of computing resources when do the load forecasting by massive amounts of high dimensional power load data,the paper propose a new algorithm which introduce sequential minimal optimization（ SMO ） algorithm into the support vector（ ε- SVR ） electric load forecasting algorithms to realize the fast training of 8-SVR, and adopt the MapReduce programming framework and HBase distributed database to parallelize the improved algorithm. I choose the real power load forecasting data from EUNITE competition, make some experiments on the Cloud computing, the experimental results show that under the prediction accuracy increased by at least 5%, the parallel performance of the proposed algorithm is better.

关 键 词：云计算 MAPREDUCE框架 电力负荷预测 支持向量回归 序列最小优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]