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Building Simulation 2011, 4(2): 117-134 https://doi.org/10.1007/s12273-011-0037-4
Research Article | Issue | Published: 04 December 2011

An algorithm to represent occupant use of windows and fans including situation-specific motivations and constraints

Show Author's Information Hom B. Rijal1( ),  Paul Tuohy2,  Michael A. Humphreys3,  J. Fergus Nicol3,  Aizaz Samuel2
Department of Environmental & Information Studies, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama, 224-8551 Japan
Energy Systems Research Unit, University of Strathclyde, Glasgow G1 1XJ, UK
Oxford Institute for Sustainable Development, Oxford Brookes University, Oxford OX3 0BP, UK
Keywords:
simulation, thermal comfort, constraints, window, fan, adaptive algorithm
Cite this article:
Rijal HB, Tuohy P, Humphreys MA, et al. An algorithm to represent occupant use of windows and fans including situation-specific motivations and constraints. Building Simulation, 2011, 4(2): 117-134. https://doi.org/10.1007/s12273-011-0037-4
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Abstract Full text About this article

A theoretical model of the interaction between a building and its occupants is developed based on field survey data; the role of the model in building performance simulation is illustrated. If free to do so, people adjust their clothing or available building controls (windows, blinds, doors, fans, and thermostats) with the aim of achieving or restoring comfort and reducing discomfort. Initially responses to thermal conditions are considered. Trigger temperatures are established where responses to warm or cold thermal discomfort may occur. These trigger-temperatures depend on (among other things) clothing (which may depend on season and social conditions) and air movement (e.g., fan setting). Trigger-temperatures differ from person to person and from time to time. If several controls are available people will use those that are most user-friendly, effective and free from undesirable consequences, and this is represented in the model by a constraint assigned to each control option. The concept of constraints is then expanded to capture non-thermal stimuli for control use (e.g., fresh-air). Using datasets from surveys in Europe and Pakistan, estimates are made of the parameters used in the model: the comfort temperature in relation to the prevailing outdoor temperature, the extent of inter-personal variation of trigger temperature, the effect of a fan on the comfort temperature, and the values of constraints that affect the use of windows and fans in the surveyed buildings. The incorporation of the new model, including constraints, into building simulation code is illustrated. Some limitations or unknowns in the current model are identified and possible approaches for future research to fill these gaps suggested. The application of the model in building performance analysis and building design is discussed.


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About this article

An algorithm to represent occupant use of windows and fans including situation-specific motivations and constraints

Show Author's information Hom B. Rijal1( ), Paul Tuohy2, Michael A. Humphreys3, J. Fergus Nicol3, Aizaz Samuel2
Department of Environmental & Information Studies, Tokyo City University, 3-3-1 Ushikubo-nishi, Tsuzuki-ku, Yokohama, 224-8551 Japan
Energy Systems Research Unit, University of Strathclyde, Glasgow G1 1XJ, UK
Oxford Institute for Sustainable Development, Oxford Brookes University, Oxford OX3 0BP, UK

Abstract

A theoretical model of the interaction between a building and its occupants is developed based on field survey data; the role of the model in building performance simulation is illustrated. If free to do so, people adjust their clothing or available building controls (windows, blinds, doors, fans, and thermostats) with the aim of achieving or restoring comfort and reducing discomfort. Initially responses to thermal conditions are considered. Trigger temperatures are established where responses to warm or cold thermal discomfort may occur. These trigger-temperatures depend on (among other things) clothing (which may depend on season and social conditions) and air movement (e.g., fan setting). Trigger-temperatures differ from person to person and from time to time. If several controls are available people will use those that are most user-friendly, effective and free from undesirable consequences, and this is represented in the model by a constraint assigned to each control option. The concept of constraints is then expanded to capture non-thermal stimuli for control use (e.g., fresh-air). Using datasets from surveys in Europe and Pakistan, estimates are made of the parameters used in the model: the comfort temperature in relation to the prevailing outdoor temperature, the extent of inter-personal variation of trigger temperature, the effect of a fan on the comfort temperature, and the values of constraints that affect the use of windows and fans in the surveyed buildings. The incorporation of the new model, including constraints, into building simulation code is illustrated. Some limitations or unknowns in the current model are identified and possible approaches for future research to fill these gaps suggested. The application of the model in building performance analysis and building design is discussed.

Keywords: simulation, thermal comfort, constraints, window, fan, adaptive algorithm

References(30)

ASHRAE (2004). ANSI/ASHRAE Standard 55-2004, Thermal Environ-mental Conditions for Human Occupancy, Atlanta: American Society of Heating, Refrigerating, and Air conditioning Engineers.
CEN 15251 (2007). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. Brussels: Comité Européen de Normalisation.
CIBSE (2006). Guide A: Environmental Design, 7th edn. London: Chartered Institution of Building Services Engineers.
JA Clarke (2001). Energy Simulation in Building Design, 2nd edn. London: Butterworth-Heinemann.
RJ de Dear, GS Brager (1998). Developing an adaptive model of thermal comfort and preference. ASHRAE Transaction, 104(1): 145-167.
Google Scholar
ID Griffths (1990). Thermal comfort in buildings with passive solar features: Field studies, Report to the Commission of the European Communities, EN3S-090, UK.
F Haldi, D Robinson (2010). On the unification of thermal perception and adaptive actions. Building and Environment, 45: 2440-2457.
DOI Google Scholar
MA Humphreys (1973). Classroom temperature, clothing and thermal comfort—a study of secondary school children in summertime. Journal of the Institute of Heating and Ventilation Engineers, 41: 191-202.
Google Scholar
MA Humphreys (1978). Outdoor temperatures and comfort indoors. Building Research and Practice (J. CIB), 6: 92-105.
DOI Google Scholar
MA Humphreys, JF Nicol (1998). Understanding the adaptive approach to thermal comfort. ASHRAE Transactions, 104(1): 991-1004.
Google Scholar
MA Humphreys, JF Nicol (2002). The validity of ISO-PMV for predicting comfort votes in every-day life. Energy and Buildings, 34: 667-684.
DOI Google Scholar
MA Humphreys, HB Rijal, JF Nicol, P Tuohy (2008). Modelling window-opening and the use of other building controls. In: Proceedings of the 29th AIVC Conference on Advanced Building Ventilation and Environmental Technology for Addressing Climate Change Issues (Vol. 3, pp. 91-97), Kyoto, Japan.
MA Humphreys, HB Rijal, JF Nicol (2010). Examining and developing the adaptive relation between climate and thermal comfort indoors. In: Proceedings of Conference: Adapting to Change: New Thinking on Comfort. Cumberland Lodge, UK. London: Network for Comfort and Energy Use in Buildings.
DRG Hunt (1979). The use of artificial lighting in relation to daylight levels and occupancy. Building and Environment, 14: 21-33.
DOI Google Scholar
KJ McCartney, JF Nicol (2002). Developing an adaptive control algorithm for Europe. Energy and Buildings, 34: 623-635.
DOI Google Scholar
F Nicol, GN Jamy, O Sykes, M Humphreys, S Roaf, M Hancock (1994). A survey of thermal comfort in Pakistan toward new indoor temperature standards. Oxford Brookes University.
JF Nicol (1974) An analysis of some observations of thermal comfort in Roorkee, India and Baghdad, Iraq. Annals of Human Biology, 1: 411-426.
DOI Google Scholar
JF Nicol, MA Humphreys (2004). A stochastic approach to thermal comfort—occupant behavior and energy use in buildings. ASHRAE Transactions, 110(2): 554-568.
Google Scholar
F Nicol, M Humphreys (2010). Derivation of the equations for comfort in free-running buildings in CEN Standard EN15251. Building and Environment, 45: 11-17.
DOI Google Scholar
JF Nicol, IA Raja, A Allaudin, GN Jamy (1999). Climatic variations in comfortable temperatures: the Pakistan projects. Energy and Buildings, 30: 261-279.
DOI Google Scholar
IA Raja, JF Nicol, KJ McCartney, MA Humphreys (2001). Thermal comfort: use of controls in naturally ventilated buildings. Energy and Buildings, 33: 235-244.
DOI Google Scholar
HB Rijal, P Tuohy, MA Humphreys, JF Nicol, A Samuel, J Clarke (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39: 823-836.
DOI Google Scholar
HB Rijal, P Tuohy, F Nicol, MA Humphreys, A Samuel, J Clarke (2008a). Development of an adaptive window-opening algorithm to predict the thermal comfort, energy use and overheating in buildings. Journal of Building Performance Simulation, 1: 17-30.
DOI Google Scholar
HB Rijal, P Tuohy, MA Humphreys, JF Nicol, A Samuel, IA Raja, J Clarke (2008b). Development of adaptive algorithms for the operation of windows, fans and doors to predict thermal comfort and energy use in Pakistani buildings. ASHRAE Transactions, 114(2): 555-573.
Google Scholar
HB Rijal, MA Humphreys, JF Nicol (2008c). Development of behavioural algorithms for thermal simulation of buildings—Evaluating constraints on the operation of windows and fans. In: Proceedings of the 29th AIVC Conference on Advanced Building Ventilation and Environmental Technology for Addressing Climate Change Issues (Vol. 3, pp. 99-104), Kyoto, Japan.
HB Rijal, MA Humphreys, JF Nicol (2009). Understanding occupant behaviour: the use of controls in mixed-mode office buildings. Building Research and Information, 37: 381-396.
DOI Google Scholar
D Robinson, F Haldi (2008). An integrated adaptive model for overheating risk prediction. Journal of Building Performance Simulation, 1: 43-55.
DOI Google Scholar
PG Tuohy, MA Humphreys, JF Nicol, HB Rijal, JA Clarke (2009). Occupant behavior in naturally ventilated and hybrid buildings. ASHRAE Transactions, 115(1), 16-27.
Google Scholar
PG Tuohy, S Roaf, JF Nicol, MA Humphreys, A Boerstra (2010). 21st century standards for thermal comfort: fostering low carbon building design and operation. Architectural Science Review, 53: 78-89.
DOI Google Scholar
GY Yun, K Steemers (2008). Time-dependent occupant behaviour models of window control in summer. Building and Environment, 43: 1471-1482.
DOI Google Scholar
Publication history
Copyright

Publication history

Received: 26 January 2011
Revised: 27 April 2011
Accepted: 03 May 2011
Published: 04 December 2011
Issue date: June 2011

Copyright

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011
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