Deducing Energy Consumer Behavior from Smart Meter Data
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 resu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-07-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/9/3/29 |
| _version_ | 1811295732204830720 |
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| author | Emad Ebeid Rune Heick Rune Hylsberg Jacobsen |
| author_facet | Emad Ebeid Rune Heick Rune Hylsberg Jacobsen |
| author_sort | Emad Ebeid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:37:04Z |
| format | Article |
| id | doaj.art-dbc7e05f4ba3407e85b73d4ea4c9a64d |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-04-13T05:37:04Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-dbc7e05f4ba3407e85b73d4ea4c9a64d2022-12-22T03:00:14ZengMDPI AGFuture Internet1999-59032017-07-01932910.3390/fi9030029fi9030029Deducing Energy Consumer Behavior from Smart Meter DataEmad Ebeid0Rune Heick1Rune Hylsberg Jacobsen2The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, DenmarkDepartment of Engineering, Aarhus University, 8200 Aarhus, DenmarkDepartment of Engineering, Aarhus University, 8200 Aarhus, DenmarkThe 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.https://www.mdpi.com/1999-5903/9/3/29smart gridsNon-Intrusive Load Monitoringmachine learningsmart metersUnified Modeling Language |
| spellingShingle | Emad Ebeid Rune Heick Rune Hylsberg Jacobsen Deducing Energy Consumer Behavior from Smart Meter Data Future Internet smart grids Non-Intrusive Load Monitoring machine learning smart meters Unified Modeling Language |
| title | Deducing Energy Consumer Behavior from Smart Meter Data |
| title_full | Deducing Energy Consumer Behavior from Smart Meter Data |
| title_fullStr | Deducing Energy Consumer Behavior from Smart Meter Data |
| title_full_unstemmed | Deducing Energy Consumer Behavior from Smart Meter Data |
| title_short | Deducing Energy Consumer Behavior from Smart Meter Data |
| title_sort | deducing energy consumer behavior from smart meter data |
| topic | smart grids Non-Intrusive Load Monitoring machine learning smart meters Unified Modeling Language |
| url | https://www.mdpi.com/1999-5903/9/3/29 |
| work_keys_str_mv | AT emadebeid deducingenergyconsumerbehaviorfromsmartmeterdata AT runeheick deducingenergyconsumerbehaviorfromsmartmeterdata AT runehylsbergjacobsen deducingenergyconsumerbehaviorfromsmartmeterdata |