Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.
Residential electrical consumption disaggregation on a single low-cost meter / Tesfaye, Mengistu Tekalign; Nardello, Matteo; Brunelli, Davide. - ELETTRONICO. - (2017), pp. 1-6. (Intervento presentato al convegno 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2017 tenutosi a Italy nel 2017) [10.1109/EESMS.2017.8052678].
Residential electrical consumption disaggregation on a single low-cost meter
Nardello, Matteo;Brunelli, Davide
2017-01-01
Abstract
Demand and cost of electricity is expected to grow in the next years. This has raised interest in monitoring energy usage to reduce losses, and to provide real-time feedback about the cost of the electrical power consumed. This paper focuses on the implementation of a stand-alone system capable of real-time tracking of the power used and that provides power consumption estimation for each device from a single point of measurement. The learning activity is done by detecting the possible state of the electrical devices using a clustering algorithm, which involves k-means technique to analyze and detect the state of an appliance.File | Dimensione | Formato | |
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