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A Framework for Detecting and Translating User Behavior from Smart Main Meter Data

Authors:
Egon Kidmose
Emad Ebeid
Rune Hylsberg Jacobsen

Keywords: Nonintrusive Appliance Load Monitoring; Machine Learning; Smart Meters; UML.

Abstract:
The European adoption of smart electricity meters triggers the developments of new value-added service for smart energy and optimal consumption. Recently, several algorithms and tools have been built to analyze smart meter's data. This paper introduces an open framework and prototypes for detecting and presenting user behavior from its smart meter power consumption data. The framework aims at presenting the detected user behavior in natural language reports. In order to validate the proposed framework, an experiment has been performed and the results have been presented.

Pages: 71 to 74

Copyright: Copyright (c) IARIA, 2015

Publication date: June 21, 2015

Published in: conference

ISSN: 2308-3727

ISBN: 978-1-61208-414-5

Location: Brussels, Belgium

Dates: from June 21, 2015 to June 26, 2015