计及用电模式的居民负荷梯度提升树分类识别方法

摘要

居民负荷分类与识别是负荷监测与需求侧管理的研究基础.为了实现居民负荷用电模式的提取和识别,本文对负荷公共数据集运用主成分分析降维并聚类,提出了一种计及典型用电模式的梯度提升树负荷分类识别方法.首先对负荷公共数据集重采样并获得各类负荷能耗特征样本,归一化后通过主成分分析法降维得到特征的主成分.再通过改进K均值聚类法获得各类负荷的典型用电模式,训练梯度提升树并进行超参数优化,对测试集负荷类型进行识别.在公共数据集与实测数据上测试发现,该方法对于居民负荷分类识别有良好效果,能够实现对负荷的分类识别.%Classification and identification for residential load are the basis of load monitoring and demand-side manage?ment. In order to realize the extraction and identification of the load power consumption modes for residents ,a gradient boosting decision tree method for load classification and identification,which takes typical power consumption modes into account,is proposed in this paper by using principal component analysis(PCA)to deduce and aggregate the com?mon dataset of load. Firstly,the load data are resampled and the characteristic samples of various types of load energy consumption data are obtained,which are further normalized and reduced by PCA to acquire the principal components. Secondly,by means of improved K-means clustering method,typical power consumption modes are obtained. More?over,gradient boosting decision tree is trained with super-parameter optimization,and the test set is used to identify dif?ferent types of loads. Through the tests on the common dataset and measured data,it is found that the proposed method has good effect on load classification and identification.