A Framework for Detecting and Translating User Behavior from Smart Meter Data
Egon Kidmose, Emad Ebeid, Rune Hylsberg Jacobsen

TL;DR
This paper presents an open framework and prototypes for analyzing smart meter data to detect user behavior and generate natural language reports, aiding in smart energy management.
Contribution
It introduces a novel open framework for detecting and translating user behavior from smart meter data into natural language reports.
Findings
Successful validation through an experiment
Effective detection of user behavior patterns
Generation of understandable natural language reports
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.
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Taxonomy
TopicsSmart Grid Energy Management · Data Stream Mining Techniques · Electricity Theft Detection Techniques
