Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth

Carlo Manna, Nic Wilson, Kenneth N. Brown

2013

Abstract

A personalized thermal comfort prediction method is proposed for use in combination with smart controls for building automation. Occupant thermal comfort is traditionally measured and predicted by the Predicted Mean Vote (PMV) metric, which is based on extensive field trials linking reported comfort levels with the various factors. However, PMV is a statistical measure applying to large populations, and the actual thermal comfort could be significantly different from the predicted value for small groups of people. Moreover it may be hard to use for a real-time controller due to the number of sensor readings needed. In the present paper, we propose Robust Locally Weighted Regression with Adaptive Bandwidth (LRAB), a kernel based method, to learn individual occupant thermal comfort based on historical reports. Using publicly available datasets, we demonstrate that this technique is significantly more accurate in predicting individual comfort than PMV and other kernel methods. Therefore, is a promising technique to be used as input to adpative HVAC control systems.

Paper Citation

in Harvard Style

Manna C., Wilson N. and Brown K. (2013). Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth . In Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS, ISBN 978-989-8565-55-6, pages 32-40. DOI: 10.5220/0004375100320040

in Bibtex Style

@conference{smartgreens13,
author={Carlo Manna and Nic Wilson and Kenneth N. Brown},
title={Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth},
booktitle={Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,},
year={2013},
pages={32-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004375100320040},
isbn={978-989-8565-55-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems - Volume 1: SMARTGREENS,
TI - Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth
SN - 978-989-8565-55-6
AU - Manna C.
AU - Wilson N.
AU - Brown K.
PY - 2013
SP - 32
EP - 40
DO - 10.5220/0004375100320040