Tundis, Andrea ; Faizan, Ali ; Mühlhäuser, Max (2023)
A Feature-Based Model for the Identification of Electrical Devices in Smart Environments.
In: Sensors, 2019, 19 (11)
doi: 10.26083/tuprints-00015507
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Tundis, Andrea ; Faizan, Ali ; Mühlhäuser, Max |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | A Feature-Based Model for the Identification of Electrical Devices in Smart Environments |
| Sprache: | Englisch |
| Publikationsjahr: | 1 Dezember 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2019 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Sensors |
| Jahrgang/Volume einer Zeitschrift: | 19 |
| (Heft-)Nummer: | 11 |
| Kollation: | 20 Seiten |
| DOI: | 10.26083/tuprints-00015507 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/15507 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided. |
| Freie Schlagworte: | electrical devices, classification, energy management, machine learning, smart environment |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-155071 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Telekooperation |
| Hinterlegungsdatum: | 01 Dez 2023 14:18 |
| Letzte Änderung: | 04 Dez 2023 12:03 |
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| Zugehörige Links: | |
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| Suche nach Titel in: | TUfind oder in Google |
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- A Feature-Based Model for the Identification of Electrical Devices in Smart Environments. (deposited 01 Dez 2023 14:18) [Gegenwärtig angezeigt]
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