Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior o...

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Autores principales:	Christina Koutroumpina, Spyros Sioutas, Stelios Koutroubinas, Kostas Tsichlas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	energy disaggregation supervised machine learning classification Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2ced8eb693024f6db5dbb89421e1ca4c
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spelling	oai:doaj.org-article:2ced8eb693024f6db5dbb89421e1ca4c2021-11-25T16:13:00ZEvaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter10.3390/a141103111999-4893https://doaj.org/article/2ced8eb693024f6db5dbb89421e1ca4c2021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/311https://doaj.org/toc/1999-4893The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.Christina KoutroumpinaSpyros SioutasStelios KoutroubinasKostas TsichlasMDPI AGarticleenergy disaggregationsupervised machine learningclassificationIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 311, p 311 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy disaggregation supervised machine learning classification Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	energy disaggregation supervised machine learning classification Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Christina Koutroumpina Spyros Sioutas Stelios Koutroubinas Kostas Tsichlas Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
description	The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.
format	article
author	Christina Koutroumpina Spyros Sioutas Stelios Koutroubinas Kostas Tsichlas
author_facet	Christina Koutroumpina Spyros Sioutas Stelios Koutroubinas Kostas Tsichlas
author_sort	Christina Koutroumpina
title	Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
title_short	Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
title_full	Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
title_fullStr	Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
title_full_unstemmed	Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter
title_sort	evaluation of features generated by a high-end low-cost electrical smart meter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2ced8eb693024f6db5dbb89421e1ca4c
work_keys_str_mv	AT christinakoutroumpina evaluationoffeaturesgeneratedbyahighendlowcostelectricalsmartmeter AT spyrossioutas evaluationoffeaturesgeneratedbyahighendlowcostelectricalsmartmeter AT stelioskoutroubinas evaluationoffeaturesgeneratedbyahighendlowcostelectricalsmartmeter AT kostastsichlas evaluationoffeaturesgeneratedbyahighendlowcostelectricalsmartmeter
_version_	1718413247013453824

Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

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