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Future Internet 2017, 9(3), 29;

Deducing Energy Consumer Behavior from Smart Meter Data

The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark
Department of Engineering, Aarhus University, 8200 Aarhus, Denmark
This paper is an extended version of our paper published in IEEE International Conference on Smart Grid Communications (SmartGridComm), 2016, under title: Presenting User Behavior from Main Meter Data.
Author to whom correspondence should be addressed.
Academic Editor: Dino Giuli
Received: 2 June 2017 / Revised: 23 June 2017 / Accepted: 26 June 2017 / Published: 6 July 2017
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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. View Full-Text
Keywords: smart grids; Non-Intrusive Load Monitoring; machine learning; smart meters; Unified Modeling Language smart grids; Non-Intrusive Load Monitoring; machine learning; smart meters; Unified Modeling Language

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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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Ebeid, E.; Heick, R.; Jacobsen, R.H. Deducing Energy Consumer Behavior from Smart Meter Data. Future Internet 2017, 9, 29.

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