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Article Dans Une Revue Journal of Sensors Année : 2019

Estimating Occupancy from Measurements and Knowledge Using Bayesian Network for Energy Management

Résumé

A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.

Dates et versions

hal-02111574 , version 1 (26-04-2019)

Identifiants

Citer

Manar Amayri, Stéphane Ploix, Hussain Kazmi, Quoc-Dung Ngo, Abed Safadi. Estimating Occupancy from Measurements and Knowledge Using Bayesian Network for Energy Management. Journal of Sensors, 2019, 12, pp.7129872. ⟨10.1155/2019/7129872⟩. ⟨hal-02111574⟩
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