Mobility Map Inference from Thermal Modeling of a Building
- URL: http://arxiv.org/abs/2011.07372v1
- Date: Sat, 14 Nov 2020 19:19:03 GMT
- Title: Mobility Map Inference from Thermal Modeling of a Building
- Authors: Risul Islam, Andrey Lokhov, Nathan Lemons, Michalis Faloutsos
- Abstract summary: We consider the problem of inferring the mobility map, which is the distribution of the building occupants at each, from the temperatures of the rooms.
Our proposed algorithm tackles down the aforementioned challenges leveraging a parameter learner, the modified Least Square Estimator.
Our work can be used in a wide range of applications, for example, ensuring the physical security of office buildings.
- Score: 1.5522829321999745
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We consider the problem of inferring the mobility map, which is the
distribution of the building occupants at each timestamp, from the temperatures
of the rooms. We also want to explore the effects of noise in the temperature
measurement, room layout, etc. in the reconstruction of the movement of people
within the building. Our proposed algorithm tackles down the aforementioned
challenges leveraging a parameter learner, the modified Least Square Estimator.
In the absence of a complete data set with mobility map, room and ambient
temperatures, and HVAC data in the public domain, we simulate a physics-based
thermal model of the rooms in a building and evaluate the performance of our
inference algorithm on this simulated data. We find an upper bound of the noise
standard deviation (<= 1F) in the input temperature data of our model. Within
this bound, our algorithm can reconstruct the mobility map with a reasonable
reconstruction error. Our work can be used in a wide range of applications, for
example, ensuring the physical security of office buildings, elderly and infant
monitoring, building resources management, emergency building evacuation, and
vulnerability assessment of HVAC data. Our work brings together multiple
research areas, Thermal Modeling and Parameter Estimation, towards achieving a
common goal of inferring the distribution of people within a large office
building.
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