Generating empirical probabilities of metabolic rate and clothing insulation values in field studies using wearable sensors
Generating empirical probabilities of metabolic rate and clothing insulation values in field studies using wearable sensors
This research introduces a mixed-method framework to estimate metabolic rate and clothing insulation as objective and quantitative variables. Methods included automated visual diaries and both environmental and wearable sensors. Applying this framework in an exploratory study, during the winters of 2012 and 2013, allowed empirical probabilities of metabolic rate and clothing insulation values to be generated. The results indicate that current standards overestimate winter clothing insulation by 22% but underestimate residential metabolic activity by 9%. Beyond reviewing the standards thresholds, these probability distributions may be used as input to building energy simulation (BES) programs.
Thermal comfort, Predictive indices, Occupant behaviour, Mixed-methods, Ubiquitous sensor technologies.
Gauthier, S.
4e7702f7-e1a9-4732-8430-fabbed0f56ed
Shipworth, D.
f0c2fd64-352f-48f3-b518-e240b4801f2e
July 2014
Gauthier, S.
4e7702f7-e1a9-4732-8430-fabbed0f56ed
Shipworth, D.
f0c2fd64-352f-48f3-b518-e240b4801f2e
Gauthier, S. and Shipworth, D.
(2014)
Generating empirical probabilities of metabolic rate and clothing insulation values in field studies using wearable sensors.
13th International Conference on Indoor Air Quality and Climate, Hong Kong.
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Conference or Workshop Item
(Paper)
Abstract
This research introduces a mixed-method framework to estimate metabolic rate and clothing insulation as objective and quantitative variables. Methods included automated visual diaries and both environmental and wearable sensors. Applying this framework in an exploratory study, during the winters of 2012 and 2013, allowed empirical probabilities of metabolic rate and clothing insulation values to be generated. The results indicate that current standards overestimate winter clothing insulation by 22% but underestimate residential metabolic activity by 9%. Beyond reviewing the standards thresholds, these probability distributions may be used as input to building energy simulation (BES) programs.
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Paper_HP0418_v02.pdf
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Published date: July 2014
Venue - Dates:
13th International Conference on Indoor Air Quality and Climate, Hong Kong, 2014-06-30
Keywords:
Thermal comfort, Predictive indices, Occupant behaviour, Mixed-methods, Ubiquitous sensor technologies.
Organisations:
Energy & Climate Change Group
Identifiers
Local EPrints ID: 378791
URI: http://eprints.soton.ac.uk/id/eprint/378791
PURE UUID: 923f0ad7-f8fe-492b-b8d9-929bae1cd7a7
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Date deposited: 21 Jul 2015 11:20
Last modified: 09 Jan 2022 03:49
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Contributors
Author:
D. Shipworth
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