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Building Simulation 2020, 13(3): 527-542 https://doi.org/10.1007/s12273-020-0615-4
Research Article | Issue | Published: 01 April 2020

Verification of behavioural models of window opening: The accuracy of window-use pattern, indoor temperature and indoor PM2.5 concentration prediction

Show Author's Information Xuyang Zhong1,2( ),  Ian A. Ridley2
Department of Civil Engineering, Lishui University, Lishui, 323000, China
School of Energy and Environment, City University of Hong Kong, Hong Kong, China
Keywords:
building simulation, indoor temperature, window opening, behavioural models, indoor PM2.5 concentration
Cite this article:
Zhong X, Ridley IA. Verification of behavioural models of window opening: The accuracy of window-use pattern, indoor temperature and indoor PM2.5 concentration prediction. Building Simulation, 2020, 13(3): 527-542. https://doi.org/10.1007/s12273-020-0615-4
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Abstract Full text Electronic supplementary material About this article

Occupant-controlled window opening plays a significant role in exposure to indoor environmental hazards such as high temperatures and PM2.5. Both the indoor and outdoor environments can affect window opening behaviour of building occupants, while variation in this behaviour is often poorly defined in building simulations. This study examines the robustness of six existing behavioural models of window opening in predicting window-use patterns in Hong Kong flats, and the impact of behavioural models on the accuracy of indoor temperature and indoor PM2.5 concentration prediction. The behavioural models were implemented in the EMS application of EnergyPlus. The predicted window-use patterns were compared to those of filed measurements, whilst the predicted indoor temperatures and indoor PM2.5 concentrations were benchmarked against those predicted by the EnergyPlus model with the actual occupant window opening behaviour. Results indicate that the ability of individual behavioural models to accurately predict occupant window opening behaviour varies depending on modelling approach, outdoor environmental variable and simulation time-step size. The model providing the best estimate of the duration of window-opening is able to best contribute to accurate predictions about indoor temperatures and indoor PM2.5 concentrations. Results also show that a static temperature threshold for window-opening (a common method of modelling occupant window opening behaviour) can lead to a significant difference between the actual and predicted window-use patterns (and therefore indoor temperatures or indoor PM2.5 concentrations), underlining the importance of improving model inputs for occupant window opening behaviour.


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Electronic supplementary material
About this article

Verification of behavioural models of window opening: The accuracy of window-use pattern, indoor temperature and indoor PM2.5 concentration prediction

Show Author's information Xuyang Zhong1,2( ), Ian A. Ridley2
Department of Civil Engineering, Lishui University, Lishui, 323000, China
School of Energy and Environment, City University of Hong Kong, Hong Kong, China

Abstract

Occupant-controlled window opening plays a significant role in exposure to indoor environmental hazards such as high temperatures and PM2.5. Both the indoor and outdoor environments can affect window opening behaviour of building occupants, while variation in this behaviour is often poorly defined in building simulations. This study examines the robustness of six existing behavioural models of window opening in predicting window-use patterns in Hong Kong flats, and the impact of behavioural models on the accuracy of indoor temperature and indoor PM2.5 concentration prediction. The behavioural models were implemented in the EMS application of EnergyPlus. The predicted window-use patterns were compared to those of filed measurements, whilst the predicted indoor temperatures and indoor PM2.5 concentrations were benchmarked against those predicted by the EnergyPlus model with the actual occupant window opening behaviour. Results indicate that the ability of individual behavioural models to accurately predict occupant window opening behaviour varies depending on modelling approach, outdoor environmental variable and simulation time-step size. The model providing the best estimate of the duration of window-opening is able to best contribute to accurate predictions about indoor temperatures and indoor PM2.5 concentrations. Results also show that a static temperature threshold for window-opening (a common method of modelling occupant window opening behaviour) can lead to a significant difference between the actual and predicted window-use patterns (and therefore indoor temperatures or indoor PM2.5 concentrations), underlining the importance of improving model inputs for occupant window opening behaviour.

Keywords: building simulation, indoor temperature, window opening, behavioural models, indoor PM2.5 concentration

References(37)

Ai ZT, Mak CM, Cui DJ, Xue P (2016). Ventilation of air-conditioned residential buildings: A case study in Hong Kong. Energy and Buildings, 127: 116-127.
DOI Google Scholar
Andersen R, Fabi V, Toftum J, Corgnati SP, Olesen BW (2013). Window opening behaviour modelled from measurements in Danish dwellings. Building and Environment, 69: 101-113.
DOI Google Scholar
Andersen RK, Fabi V, Corgnati SP (2016). Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings. Energy and Buildings, 127: 105-115.
DOI Google Scholar
Borgeson S, Brager G (2008). Occupant control of windows: Accounting for human behavior in building simulation. CBE Internal Report. Center for the Built Environment.
Calì D, Andersen RK, Müller D, Olesen BW (2016). Analysis of occupants’ behavior related to the use of windows in German households. Building and Environment, 103: 54-69.
DOI Google Scholar
Chan L, Kwok W (2000). Vertical dispersion of suspended particulates in urban area of Hong Kong. Atmospheric Environment, 34: 4403-4412.
DOI Google Scholar
CIBSE (2006). CIBSE guide A. Environmental design. Chartered Institution of Building Services Engineers (CIBSE).
DOI
Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang Y, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319-331.
DOI Google Scholar
Dimitroulopoulou C, Ashmore MR, Hill MTR, Byrne MA, Kinnersley R (2006). INDAIR: A probabilistic model of indoor air pollution in UK homes. Atmospheric Environment, 40: 6362-6379.
DOI Google Scholar
Fabi V, Andersen RV, Corgnati S, Olesen BW (2012). Occupants’ window opening behaviour: A literature review of factors influencing occupant behaviour and models. Building and Environment, 58: 188-198.
DOI Google Scholar
Fabi V, Andersen RK, Corgnati S (2015). Verification of stochastic behavioural models of occupants’ interactions with windows in residential buildings. Building and Environment, 94: 371-383.
DOI Google Scholar
Fritsch R, Kohler A, Nygård-Ferguson M, Scartezzini JL (1990). A stochastic model of user behaviour regarding ventilation. Building and Environment, 25: 173-181.
DOI Google Scholar
Gunay HB, O’Brien W, Beausoleil-Morrison I (2016). Implementation and comparison of existing occupant behaviour models in EnergyPlus. Journal of Building Performance Simulation, 9: 567-588.
DOI Google Scholar
Gunay HB, O’Brien W, Beausoleil-Morrison I, Gilani S (2017). Development and implementation of an adaptive lighting and blinds control algorithm. Building and Environment, 113: 185-199.
DOI Google Scholar
Haldi F, Robinson D (2008). On the behaviour and adaptation of office occupants. Building and Environment, 43: 2163-2177.
DOI Google Scholar
Haldi F, Robinson D (2009). Interactions with window openings by office occupants. Building and Environment, 44: 2378-2395.
DOI Google Scholar
Haldi F, Robinson D, Pröglhöf C, et al. (2010). A partial double blind evaluation of a comprehensive window opening model. In: Proceedings of BauSIM 2010 Conference, pp. 331–336.
Herkel S, Knapp U, Pfafferott J (2008). Towards a model of user behaviour regarding the manual control of windows in office buildings. Building and Environment, 43: 588-600.
DOI Google Scholar
Jones N, Thornton C, Mark D, Harrison R (2000). Indoor/outdoor relationships of particulate matter in domestic homes with roadside, urban and rural locations. Atmospheric Environment, 34: 2603-2612.
DOI Google Scholar
Jones B, Das P, Chalabi Z, Davies M, Hamilton I, et al. (2013). The effect of party wall permeability on estimations of infiltration from air leakage. International Journal of Ventilation, 12: 17-30.
DOI Google Scholar
Jones RV, Fuertes A, Gregori E, Giretti A (2017). Stochastic behavioural models of occupants’ main bedroom window operation for UK residential buildings. Building and Environment, 118: 144-158.
DOI Google Scholar
Lai HK, Bayer-Oglesby L, Colvile R, Götschi T, Jantunen MJ, et al. (2006). Determinants of indoor air concentrations of PM2.5, black smoke and NO2 in six European cities (EXPOLIS study). Atmospheric Environment, 40: 1299-1313.
DOI Google Scholar
Long CM, Suh HH, Catalano PJ, Koutrakis P (2001). Using time- and size-resolved particulate data to quantify indoor penetration and deposition behavior. Environmental Science & Technology, 35: 2089-2099.
DOI Google Scholar
Mavrogianni A, Wilkinson P, Davies M, Biddulph P, Oikonomou E (2012). Building characteristics as determinants of propensity to high indoor summer temperatures in London dwellings. Building and Environment, 55: 117-130.
DOI Google Scholar
Nicol JF, Humphreys MA (2004). A Stochastic approach to thermal comfort-occupant behavior and energy use in buildings. ASHRAE Transactions, 110(2): 554-568.
Google Scholar
O’Brien W, Gunay HB (2014). The contextual factors contributing to occupants’ adaptive comfort behaviors in offices—A review and proposed modeling framework. Building and Environment, 77: 77-87.
DOI Google Scholar
Parys W, Saelens D, Hens H (2011). Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices – a review-based integrated methodology. Journal of Building Performance Simulation, 4: 339-358.
DOI Google Scholar
Peacock AD, Jenkins DP, Kane D (2010). Investigating the potential of overheating in UK dwellings as a consequence of extant climate change. Energy Policy, 38: 3277-3288.
DOI Google Scholar
Rijal HB, Tuohy P, Humphreys MA, Nicol JF, Samuel A, Clarke J (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
Rijal HB, Tuohy P, Humphreys MA, Nicol JF, Samuel A (2012). Considering the impact of situation-specific motivations and constraints in the design of naturally ventilated and hybrid buildings. Architectural Science Review, 55: 35-48.
DOI Google Scholar
Roetzel A, Tsangrassoulis A, Dietrich U, Busching S (2010). A review of occupant control on natural ventilation. Renewable and Sustainable Energy Reviews, 14: 1001-1013.
DOI Google Scholar
Schweiker M, Haldi F, Shukuya M, Robinson D (2012). Verification of stochastic models of window opening behaviour for residential buildings. Journal of Building Performance Simulation, 5: 55-74.
DOI Google Scholar
Shi S, Zhao B (2016). Occupants’ interactions with windows in 8 residential apartments in Beijing and Nanjing, China. Building Simulation, 9: 221-231.
DOI Google Scholar
Taylor J, Shrubsole C, Davies M, Biddulph P, Das P, et al. (2014). The modifying effect of the building envelope on population exposure to PM2.5from outdoor sources. Indoor Air, 24: 639-651.
DOI Google Scholar
Yao M, Zhao B (2017). Window opening behavior of occupants in residential buildings in Beijing. Building and Environment, 124: 441-449.
DOI Google Scholar
Zhang N, Jin W, He J (2016). Experimental study on the influence of ventilated window on indoor air quality and energy consumption. Procedia Engineering, 146: 296-302.
DOI Google Scholar
Zhang T, Su Z, Wang J, Wang S (2018). Ventilation, indoor particle filtration, and energy consumption of an apartment in Northern China. Building and Environment, 143: 280-292.
DOI Google Scholar
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Publication history
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Acknowledgements

Publication history

Received: 02 May 2019
Accepted: 02 February 2020
Published: 01 April 2020
Issue date: June 2020

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Acknowledgements

This work was supported by City University of Hong Kong through the UGC-allocated funds and TA scheme (Grant No. 000618).

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