Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm
1
Department of Computer Science and Information Technology, La Trobe University, Melbourne 3086, Australia
2
Department of Software, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5236; https://doi.org/10.3390/s19235236
Received: 11 October 2019
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Revised: 26 November 2019
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Accepted: 26 November 2019
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Published: 28 November 2019
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
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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Keywords:
non-intrusive load monitoring; smart grid; smart metering; performance metrics; privacy; energy disaggregation; data collection
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MDPI and ACS Style
Desai, S.; Alhadad, R.; Mahmood, A.; Chilamkurti, N.; Rho, S. Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm. Sensors 2019, 19, 5236. https://doi.org/10.3390/s19235236
AMA Style
Desai S, Alhadad R, Mahmood A, Chilamkurti N, Rho S. Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm. Sensors. 2019; 19(23):5236. https://doi.org/10.3390/s19235236
Chicago/Turabian StyleDesai, Sanket, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti, and Seungmin Rho. 2019. "Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm" Sensors 19, no. 23: 5236. https://doi.org/10.3390/s19235236
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