基于ICPSO-XGBoost的非侵入式负荷辨识方法

针对家庭用电负荷的电气特征相近导致基于电气量特征的非侵入式负荷辨识方法易产生误辨识的问题,文中提出以电器投切时间、运行时长和投切次数为代表的电器使用规律特征,并结合传统电气负荷特征组合成为新的负荷特征标签。在此基础上,提出一种基于改进混沌粒子群优化的极端梯度提升树算法。在该算法中,首先利用回归树作为负荷特征的基分类器构建极端梯度提升树模型。进一步地,通过在目标函数中加入正则项,添加缩减系数等措施避免算法陷入过拟合。同时,将混沌思想应用于粒子群算法中提升其全局寻优能力,并得到基于改进混沌粒子群优化后的极端梯度提升树算法模型。最后,在AMPds公用数据集上进行测试,通过对比分析测试结果,验证了文中所提出的负荷特征标签和负荷辨识算法对提升非侵入式负荷辨识的有效性。

A non-instrusive load identification methodbased on ICPSO-XGBoost

Considering the misidentification of non-intrusive load identification method based on electrical features, when used for household loads which have similar electrical load features, this paper proposes features combination of household loads utilization which presented by load switching time, working period and number of switching, and original electrical features as new load identification features. On this basis, this paper presents extreme gradient boosting algorithm based on improved chaotic particle swarm. First, build extreme gradient boosting model with regression trees as base classifier. Furthermore, ease over fitting by adding regularization terms to object function and reduction coefficient. Meanwhile, chaos thought is applied to particle swarm algorithm to promote global parameter optimization, and get extreme gradient boosting algorithm with parameters improved by chaotic particle swarm. At last, we use AMPds public data set to test the algorithm this paper proposed, and the comparative analysis of testing results show the features and load identification algorithm have nice effect.