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Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the stat...

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Autores principales: Desai, Sanket, Alhadad, Rabei, Mahmood, Abdun, Chilamkurti, Naveen, Rho, Seungmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928902/
https://www.ncbi.nlm.nih.gov/pubmed/31795235
http://dx.doi.org/10.3390/s19235236
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author Desai, Sanket
Alhadad, Rabei
Mahmood, Abdun
Chilamkurti, Naveen
Rho, Seungmin
author_facet Desai, Sanket
Alhadad, Rabei
Mahmood, Abdun
Chilamkurti, Naveen
Rho, Seungmin
author_sort Desai, Sanket
collection PubMed
description With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.
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spelling pubmed-69289022019-12-26 Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm Desai, Sanket Alhadad, Rabei Mahmood, Abdun Chilamkurti, Naveen Rho, Seungmin Sensors (Basel) Article With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment. MDPI 2019-11-28 /pmc/articles/PMC6928902/ /pubmed/31795235 http://dx.doi.org/10.3390/s19235236 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Desai, Sanket
Alhadad, Rabei
Mahmood, Abdun
Chilamkurti, Naveen
Rho, Seungmin
Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title_full Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title_fullStr Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title_full_unstemmed Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title_short Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
title_sort multi-state energy classifier to evaluate the performance of the nilm algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928902/
https://www.ncbi.nlm.nih.gov/pubmed/31795235
http://dx.doi.org/10.3390/s19235236
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