Mobility Map Inference from Thermal Modeling of a Building

TL;DR

This paper presents a thermal modeling-based algorithm to infer building occupant locations from temperature data, demonstrating effective reconstruction within noise bounds through simulation, with applications in security, monitoring, and resource management.

Contribution

The paper introduces a novel inference algorithm using a modified Least Square Estimator to reconstruct mobility maps from thermal data, addressing noise and layout challenges in building environments.

Findings

Reconstruction error is reasonable within a noise standard deviation of <= 1F.

The algorithm effectively infers occupant distribution from simulated thermal data.

Potential applications include security, monitoring, and emergency response.

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.