Context-Based Energy Disaggregation in Smart Homes

Francesca Paradiso, Federica Paganelli, Dino Giuli and Samuele Capobianco
Additional contact information
Francesca Paradiso: Department of Information Engineering, University of Firenze, via S. Marta 3, 50139 Firenze, Italy
Federica Paganelli: Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) Research Unit at the University of Firenze, via S. Marta 3, 50139, Firenze, Italy
Dino Giuli: Department of Information Engineering, University of Firenze, via S. Marta 3, 50139 Firenze, Italy
Samuele Capobianco: Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT) Research Unit at the University of Firenze, via S. Marta 3, 50139, Firenze, Italy

Future Internet, 2016, vol. 8, issue 1, 1-22

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.

Keywords: energy; smart grid; smart home; metering; energy efficiency; Gaussian mixture models; Factorial Hidden Markov Models; energy disaggregation; context awareness; non intrusive load monitoring (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1999-5903/8/1/4/pdf (application/pdf)
https://www.mdpi.com/1999-5903/8/1/4/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:8:y:2016:i:1:p:4-:d:63017

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().