MC-NILM: A Multi-Chain Disaggregation Method for NILM

Hao Ma, Juncheng Jia, Xinhao Yang, Weipeng Zhu and Hong Zhang
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Hao Ma: School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Juncheng Jia: School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Xinhao Yang: School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China
Weipeng Zhu: School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Hong Zhang: School of Computer Science and Technology, Soochow University, Suzhou 215006, China

Energies, 2021, vol. 14, issue 14, 1-14

Abstract: Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k -length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.

Keywords: non-intrusive load monitoring (NILM); energy disaggregation; multi-chain disaggregation; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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