Virtual temperature measurement for smart buildings via Bayesian model fusion

Conference Paper

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