大数据环境下基于改进SVM的典型负荷类型识别

Typical Load Type Recognition Based on Improved SVM in Big Data Environment

技术的进步推动了电力大数据的发展,针对电力行业的数据挖掘研究成为当下的热点。针对传统的支持向量机(SVM)在处理大数据时存在耗费时间较长、识别效果不佳的问题,引入次梯度下降算法对支持向量机进行训练,旨在降低训练样本数目的大小对支持向量机训练时间的影响。同时借助Hadoop平台处理大数据的优势,通过构建电力负荷特性指标对原始数据降维,在MapReduce框架下随机构建多个分类器,基于投票机制划分样本类别,构建分类指标对结果进行评价。算例部分分别对比了训练样本数目的大小对分类效果的影响以及四种机器学习算法在处理电力大数据时的性能优劣,结果表明本文所用方法在电力大数据领域具有很好的分类识别效果。

Advances in technology has promoted the development of power big data and the research of data mining for the power industry has become a hot topic at present.In view of such issue of traditional support vector machine(SVM)as long consumption time and weak recognition effect in processing big data,the sub-gradient descent algorithm is introduced to train the support vector machine with the aim of reducing the influence of training sample size on training time of the support vector machine.At the same time,with the help of advantages of processing big data by Hadoop platform,the dimension of the original data is reduced by constructing power load characteristic index.The multiple classifiers are randomly constructed under the MapReduce framework and the sample type is classified on the basis of voting mechanism so to construct the classification index for result evaluation.The influence of size of the training sample on the classification effect and the performance of the four machine learning algorithms in the processing of power big data are compared by calculation respectively.The results show that the method used in this paper has a good classification and recognition effect in the field of power big data.