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Context-Based Energy Disaggregation in Smart Homes

Author

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  • 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)

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.

Suggested Citation

  • Francesca Paradiso & Federica Paganelli & Dino Giuli & Samuele Capobianco, 2016. "Context-Based Energy Disaggregation in Smart Homes," Future Internet, MDPI, vol. 8(1), pages 1-22, January.
    • Handle: RePEc:gam:jftint:v:8:y:2016:i:1:p:4-:d:63017
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    References listed on IDEAS

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    1. Mills, Bradford & Schleich, Joachim, 2012. "Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries," Energy Policy, Elsevier, vol. 49(C), pages 616-628.
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    Cited by:

    1. Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
    2. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    3. Purna Prakash Kasaraneni & Venkata Pavan Kumar Yellapragada & Ganesh Lakshmana Kumar Moganti & Aymen Flah, 2022. "Analytical Enumeration of Redundant Data Anomalies in Energy Consumption Readings of Smart Buildings with a Case Study of Darmstadt Smart City in Germany," Sustainability, MDPI, vol. 14(17), pages 1-24, August.

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