文章摘要
刘洋,许立雄.适用于海量负荷数据分类的高性能反向传播神经网络算法[J].电力系统自动化,2018,42(21):96-103. DOI: 10.7500/AEPS20171215005.
LIU Yang,XU Lixiong.High-performance Back Propagation Neural Network Algorithm for Classification of Mass Load Data[J].Automation of Electric Power Systems,2018,42(21):96-103. DOI: 10.7500/AEPS20171215005.
适用于海量负荷数据分类的高性能反向传播神经网络算法
High-performance Back Propagation Neural Network Algorithm for Classification of Mass Load Data
DOI:10.7500/AEPS20171215005
关键词: 负荷分类  Spark平台  反向传播神经网络  集成学习  聚类算法
KeyWords: load classification  Spark platform  back propagation neural network(BPNN)  ensemble learning  clustering algorithm
上网日期:2018-09-11
基金项目:
作者单位E-mail
刘洋 四川大学电气信息学院, 四川省成都市 610065  
许立雄 四川大学电气信息学院, 四川省成都市 610065 xulixiong@163.com 
摘要:
      负荷分类对于指导电网发用电规划与保证电网可靠运行具有重要意义。面向负荷数据海量化与复杂化趋势,传统负荷分类方法已无法满足用电大数据分析要求。首先,针对用户侧数据体量大、类型多、速度快等特点,在Spark平台上将反向传播神经网络(BPNN)算法并行化,实现对海量负荷数据的高效分类。然后,通过对训练样本抽样分块以降低各网络学习时间,针对分布式后BPNN基分类器由于学习样本缺失潜在的准确度下降问题,采用集成学习予以改善。并通过BPNN学习不同训练样本块构建差异化基分类器,对基分类结果多数投票得到最终分类结果。另外,提供了一种基于K-means和K-medoids聚类的负荷数据训练样本选取方法。算例表明所提方法既能对负荷曲线有效分类,又能大幅提高海量数据的处理效率。
Abstract:
      Load classification plays an important role in power grid to guide the power planning and guarantee the reliable operation of grid. However, traditional load classification is difficult to adapt to larger scale and higher dimensions of user load data. Firstly, according to the characteristics of mass load data, multiple types, high speed on the demand side, the back propagation neural network(BPNN)algorithm is improved to achieve the high efficient classification of mass load data on Spark platform. Then, the training samples are divided into blocks to reduce the learning time of each BPNN, the ensemble learning is adopted in order to compensate for the performance degradation of the single BPNN classifier due to the lack of learning samples. The final result is obtained by voting most of the sub classification results. In addition, this paper presents a training sample selection method based on K-means and K-medoids clustering algorithm. Analytical results show that the presented method can not only effectively classify load curves, but also greatly improve the processing efficiency of mass data.
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