Deducing Energy Consumer Behavior from Smart Meter Data

Emad Ebeid, Rune Heick and Rune Hylsberg Jacobsen
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Emad Ebeid: The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark
Rune Heick: Department of Engineering, Aarhus University, 8200 Aarhus, Denmark
Rune Hylsberg Jacobsen: Department of Engineering, Aarhus University, 8200 Aarhus, Denmark

Future Internet, 2017, vol. 9, issue 3, 1-25

Abstract: The ongoing upgrade of electricity meters to smart ones has opened a new market of intelligent services to analyze the recorded meter data. This paper introduces an open architecture and a unified framework for deducing user behavior from its smart main electricity meter data and presenting the results in a natural language. The framework allows a fast exploration and integration of a variety of machine learning algorithms combined with data recovery mechanisms for improving the recognition’s accuracy. Consequently, the framework generates natural language reports of the user’s behavior from the recognized home appliances. The framework uses open standard interfaces for exchanging data. The framework has been validated through comprehensive experiments that are related to an European Smart Grid project.

Keywords: smart grids; Non-Intrusive Load Monitoring; machine learning; smart meters; Unified Modeling Language (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
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