Energy Disaggregation Using Elastic Matching Algorithms

Entropy

卷:22

PascalA. Schirmer¹  Iosif Mporas¹  Michael Paraskevas² 

[1] Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;

[2] Computer Technology Institute and Press “Diophantus”, Dept of Electrical and Computer Engineering, University of Peloponnese, 221 00 Tripoli, Greece;

关键词: non-intrusive load monitoring (nilm);    energy disaggregation;    elastic matching algorithms;    dynamic time warping;    minimum variance matching;   

DOI  :  10.3390/e22010071

来源: DOAJ

【 摘 要 】

In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

【 授权许可】

Unknown