Virtual temperature measurement for smart buildings via Bayesian model fusion

Published

Conference Paper

© 2016 IEEE. 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.

Full Text

Duke Authors

Cited Authors

  • Chen, X; Li, X

Published Date

  • July 29, 2016

Published In

Volume / Issue

  • 2016-July /

Start / End Page

  • 950 - 953

International Standard Serial Number (ISSN)

  • 0271-4310

International Standard Book Number 13 (ISBN-13)

  • 9781479953400

Digital Object Identifier (DOI)

  • 10.1109/ISCAS.2016.7527399

Citation Source

  • Scopus