User behavior prediction in energy consumption in housing using Bayesian Networks
| Autor: | Lamis Hawarah, Mireille Jacomino, Stéphane Ploix |
|---|---|
| Přispěvatelé: | Perruet, Marie Josèphe, Gestion et Conduite des Systèmes de Production (G-SCOP_GCSP), Laboratoire des sciences pour la conception, l'optimisation et la production (G-SCOP), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Consumption (economics)
Service (systems architecture) General method Operations research Computer science [SPI] Engineering Sciences [physics] 020209 energy Bayesian network Home automation system 02 engineering and technology Energy consumption computer.software_genre [SPI]Engineering Sciences [physics] Order (business) 11. Sustainability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer Energy (signal processing) ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Artificial Intelligence and Soft Computing ISBN: 9783642132070 ICAISC (1) |
| Popis: | This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose user's behavior in housing. Some results and perspectives are presented in this paper. |
| Databáze: | OpenAIRE |
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