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Virtual temperature measurement for smart buildings via Bayesian model fusion

One important goal of creating smart buildings is to offer highly comfortable services to the occupants at low cost. Real-time temperature measurement and monitoring is a critical task to facilitate high-quality service with low energy consumption and, hence, cost. In this paper, we propose a novel framework to accurately measure in-building temperature by using a small number of sensors. The key idea is to combine the prior knowledge on temperature statistics with a few sensor measurements and then predict the spatial temperature distribution by maximum-a-posteriori estimation. Our experimental results demonstrate that the average estimation error is less than 0.3 degree with very few sensors.