CISUC

An Experimental Study on Electrical Signature Identification of non-intrusive load monitoring (NILM) systems

Authors

Abstract

Electrical load disambiguation for end-use recognition in the residential sector has become an area of study of its own right. Several works have shown that individual loads can be detected (and separated) from sampling of the power at a single point (e.g. the electrical service entrance for the house) using a non-intrusive load monitoring (NILM) approach. This work presents the development of an algorithm for electrical feature extraction and pattern recognition, capable of determining the individual consumption of each device from the aggregate electric signal of the home. Namely, the idea consists of analyzing the electrical signal and identifying the unique patterns that occur whenever a device is turned on or off by applying signal processing techniques. We further describe our technique for distinguishing loads by matching different signal parameters (step-changes in active and reactive powers and power factor) to known patterns. Computational experiments show the effectiveness of the proposed approach.

Keywords

feature extraction and classification, k-nearest neighbors, non-intrusive load monitoring, steady-state signatures, support vector machines

Subject

Non-Intrusive Load Monitoring Systems

Conference

Proc Intl Conf on Adaptive and Natural Computing Algorithms, pp 31-40, Part II, LNCS 6594, April 2011


Cited by

Year 2017 : 2 citations

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Year 2016 : 3 citations

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Year 2015 : 3 citations

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Year 2014 : 6 citations

 Tabatabaei, Seyed Mostafa. Decomposition Techniques for Non-intrusive Home Appliance Load Monitoring. Master Diss. University of Alberta, Canada, (2014).

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 V Debusschere, K Basu, S Bacha, Identification et prédiction non intrusive de l'état des charges dans les bâtiments résidentiels à partir de mesures compteur à échantillonnage réduit, Symposium de Génie Électrique 2014.

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Year 2013 : 3 citations

 Wong, Y. F.; Ahmet Sekercioglu, Y.; Drummond, T.; Wong, V. S., "Recent approaches to non-intrusive load monitoring techniques in residential settings," IEEE Symposium on Computational Intelligence Applications In Smart Grid, 2013, pp.73-79, 2013, doi: 10.1109/CIASG.2013.6611501

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Year 2012 : 3 citations

 Po-An Chou; Chi-Cheng Chuang; Ray-I Chang, "Automatic appliance classification for non-intrusive load monitoring," Power System Technology (POWERCON), 2012 IEEE International Conference on , vol., no., pp.1,6, Oct. 30 2012-Nov. 2 2012
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Year 2011 : 1 citations

 Oliver Parson, "Using Hidden Markov Models for Non-intrusive Appliance Load Monitoring".
Tech. Report, School of Electronics and Computer Science, Faculty of Physical and Applied Science, Univ. of Southampton, 2011.