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Communication Dans Un Congrès Année : 2021

Optimal Sensor Location in Smart Building to Estimate Occupancy While Addressing Model Uncertainties

Résumé

Nowadays, buildings are responsible for over 30% of society's energy consumption and half of the global electricity demand [1]. In order to build a sustainable and integrated energy system, it's crucial to make the buildings more intelligent not only to minimize the energy consumption while ensuring comfort but also to provide ancillary services to the energy market in the future smart energy system for the system operators and balancing party. The prerequisite for achieving the goal of the intelligent building is data collection. The accuracy, diversity, and non-repeatability of data become the key to this problem, so finding an optimization method to locate different kinds of sensors so as to obtain good data quality and to minimize the sensors number played a prominent role in data collection. This presentation first provide a short state-of-art review for various optimal sensor placement techniques. Based on the literature review, a greedy algorithm relying on the condition number of a Fisher information matrix [2] is presented in the methodology section. It is applied to find the M optimal temperature sensors placement for a typical university lecture room in west of France, for which temperature was computed at several points through energy (EnergyPlus tool) and CFD simulations. In the results section of the presentation, the set of M indoor temperature sensors that bests fit the temperature at T target points is identified. Then, an implicit model linking estimated and real temperature at some target points is designed for N occupancy scenarios. The model related error is estimated between each N cases. Then, using these model uncertainties as well as the estimated temperature at the target points, an artificial neural network is applied to predict the occupancy status (absence or presence of occupants) with a certain level of confidence. Furthermore, a more profound study of variational number (M) of sensors will be addressed to gain insight for prediction accuracy.
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Dates et versions

hal-03993777 , version 1 (17-02-2023)

Identifiants

  • HAL Id : hal-03993777 , version 1

Citer

Chuaho Jiang, Marie-Lise Pannier, David Bigaud. Optimal Sensor Location in Smart Building to Estimate Occupancy While Addressing Model Uncertainties. 31st European Safety and Reliability Conference (ESREL 2021), Sep 2021, Angers, France. pp.165/450. ⟨hal-03993777⟩
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