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Future Internet 2016, 8(1), 4; doi:10.3390/fi8010004

Context-Based Energy Disaggregation in Smart Homes

1
Department of Information Engineering, University of Firenze, via S. Marta 3, 50139 Firenze, Italy
2
Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) Research Unit at the University of Firenze, via S. Marta 3, 50139, Firenze, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Jose Ignacio Moreno Novella
Received: 26 November 2015 / Revised: 30 December 2015 / Accepted: 14 January 2016 / Published: 27 January 2016
(This article belongs to the Special Issue Ecosystemic Evolution Feeded by Smart Systems)
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Abstract

In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments. View Full-Text
Keywords: energy; smart grid; smart home; metering; energy efficiency; Gaussian mixture models; Factorial Hidden Markov Models; energy disaggregation; context awareness; non intrusive load monitoring energy; smart grid; smart home; metering; energy efficiency; Gaussian mixture models; Factorial Hidden Markov Models; energy disaggregation; context awareness; non intrusive load monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Paradiso, F.; Paganelli, F.; Giuli, D.; Capobianco, S. Context-Based Energy Disaggregation in Smart Homes. Future Internet 2016, 8, 4.

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