User behavior prediction in energy consumption in housing using Bayesian networks

Authors:
Lamis Hawarah;Stéphane Ploix;Mireille Jacomino
Affiliations:
G-SCOP Laboratory, INP Grenoble, UJF, CNRS, Grenoble, France;G-SCOP Laboratory, INP Grenoble, UJF, CNRS, Grenoble, France;G-SCOP Laboratory, INP Grenoble, UJF, CNRS, Grenoble, France
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

Citing 4
Cited 0

Fusion, propagation, and structuring in belief networks

Artificial Intelligence
Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Method to analyze user behavior in home environment

Personal and Ubiquitous Computing
Modeling and simulation of individual user behavior for building performance predictions

Proceedings of the 2007 Summer Computer Simulation Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.