Classifier economics of Semi-Intrusive Load Monitoring（SCI二区，2021年影响因子：3.588）
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影响因子：3.588
DOI码：10.1016/j.ijepes.2018.05.010
发表刊物：INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
关键字：Load monitoring, Machine learning, Classifier evaluation, Smart grid, Smart meter
摘要：Non-Intrusive Load Monitoring (NILM) and Semi-Intrusive Load Monitoring (SILM) are fast developing techniques for devices operation recognition in system monitoring. Many traditional researches focus on feature space improvements for better recognition accuracy and classifier/meter quantity reduction. But practically, cost of each classifier/meter will influence the optimal NILM/SILM solution. A feature space with better accuracy in NILM may require more cost than a SILM solution with multiple classifiers with simpler feature spaces. Facing this issue, this paper initiates a new classifier network construction method for NILM/SILM. Instead of creating a classifier for NILM or SILM, this method helps decision maker to select different types of classifiers and optimally allocates the classifiers' positions. In this method, economics of each type of classifier is considered to ensure decision maker's cost reduction. A combinatorial optimization problem is established on a tree-type model to the optimized classifier network. Numerical studies on a public data set REDD and an industrial operational data are implemented to support the feasibility of the method.
备注：SCI二区，2021年影响因子：3.588.
合写作者：Baifu Huang,Xin Cun,Fenghua Wang,Alfredo Vaccaro
第一作者：Fangyuan Xu
论文类型：期刊论文
通讯作者：Haoliang Yuan,Loi Lei Lai
卷号：103
页面范围：224-232
ISSN号：0142-0615
是否译文：否
发表时间：2018-12-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0142061518305908?via%3Dihub