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Open AccessArticle

Event Matching Classification Method for Non-Intrusive Load Monitoring

Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran 14155-6343, Iran
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Sustainability 2021, 13(2), 693; https://doi.org/10.3390/su13020693
Received: 18 November 2020 / Revised: 24 December 2020 / Accepted: 4 January 2021 / Published: 12 January 2021
(This article belongs to the Special Issue Energy Systems Integration: From Policy-Makers to Consumers)
Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters. View Full-Text
Keywords: demand-side management; clustering; event detection; non-intrusive load monitoring demand-side management; clustering; event detection; non-intrusive load monitoring
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MDPI and ACS Style

Azizi, E.; Beheshti, M.T.H.; Bolouki, S. Event Matching Classification Method for Non-Intrusive Load Monitoring. Sustainability 2021, 13, 693. https://doi.org/10.3390/su13020693

AMA Style

Azizi E, Beheshti MTH, Bolouki S. Event Matching Classification Method for Non-Intrusive Load Monitoring. Sustainability. 2021; 13(2):693. https://doi.org/10.3390/su13020693

Chicago/Turabian Style

Azizi, Elnaz; Beheshti, Mohammad T.H.; Bolouki, Sadegh. 2021. "Event Matching Classification Method for Non-Intrusive Load Monitoring" Sustainability 13, no. 2: 693. https://doi.org/10.3390/su13020693

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