Defining occupancy patterns through monitoring existing buildings
DOI:
https://doi.org/10.3989/id.53526Keywords:
Energy performance, Occupancy patterns, Occupants’ behaviour, Occupancy monitoring, Post-occupancy evaluationAbstract
Simulation programs are used to calculate the energy performance of buildings. However, numerous studies have shown a gap between calculated and actual thermal performance of buildings. One of the factors that have been identified as a source of uncertainty in building simulations is the occupancy of the building and occupants’ behaviour. These parameters are usually defined based on standards or assumed conditions. Thus, this research focuses on the occupants’ presence and behaviour in residential buildings. This paper presents an investigation on energy demand via dynamic building simulations and monitoring campaigns. The values obtained from the monitoring campaign were used as input data into the thermal simulation program and a comparison between normative and actual occupancy patterns was performed based on an occupied dwelling in Madrid, Spain.
Downloads
References
(1) Branco, G., et al., Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 2004. 36(6): p. 543-555. https://doi.org/10.1016/j.enbuild.2004.01.028
(2) De Wilde, P., The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 2014. 41: p. 40-49. https://doi.org/10.1016/j.autcon.2014.02.009
(3) Burman, E., D. Mumovic, and J. Kimpian, Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings. Energy, 2014. 77: 153-163, https://doi.org/10.1016/j.energy.2014.05.102
(4) International Energy Agency (IEA), E.A., Total energy use in buildings: analysis & evaluation methods (project factsheet). 2012.
(5) Cipriano, X., et al., Influencing factors in energy use of housing blocks: A new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects. Energy Efficiency, 2015. 10(2): 359-382. https://doi.org/10.1007/s12053-016-9460-9
(6) D'Oca, S. and T. Hong, A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 2014. 82: p. 726-739. https://doi.org/10.1016/j.buildenv.2014.10.021
(7) Herkel, S., U. Knapp, and J. Pfafferott, Towards a model of user behaviour regarding the manual control of windows in office buildings. Building and environment, 2008. 43(4): p. 588-600. https://doi.org/10.1016/j.buildenv.2006.06.031
(8) Pfafferott, J. and S. Herkel, Statistical simulation of user behaviour in low-energy office buildings. Solar Energy, 2007.81(5): p. 676-682. https://doi.org/10.1016/j.solener.2006.08.011
(9) Hoes, P., et al., User behavior in whole building simulation. Energy and Buildings, 2009. 41(3): p. 295-302. https://doi.org/10.1016/j.enbuild.2008.09.008
(10) Feng, X., D. Yan, and T. Hong, Simulation of occupancy in buildings. Energy and Buildings, 2015. 87: p. 348-359. https://doi.org/10.1016/j.enbuild.2014.11.067
(11) Andersen, R.V., et al., Survey of occupant behaviour and control of indoor environment in Danish dwellings. Energy and Buildings, 2009. 41(1): p. 11-16. https://doi.org/10.1016/j.enbuild.2008.07.004
(12) Kane, T., S.K. Firth, and K.J. Lomas, How are UK homes heated? A city-wide, socio-technical survey and implications for energy modelling. Energy and Buildings, 2015. 86: p. 817-832. https://doi.org/10.1016/j.enbuild.2014.10.011
(13) Guerra-Santin, O. and C. Tweed Aidan, In-use monitoring of buildings: an overview and classification of evaluation methods. Energy and Buildings, 2015. 86: 176-189. https://doi.org/10.1016/j.enbuild.2014.10.005
(14) Gram-Hanssen, K., Residential heat comfort practices: understanding users. Building Research & Information, 2010. 38(2): p. 175-186. https://doi.org/10.1080/09613210903541527
(15) Sendra, J.J., et al., Intervención energética en el sector residencial del sur de Espa-a: Retos actuales. Informes de la Construcción, 2013. 65(532): p. 457-464. https://doi.org/10.3989/ic.13.074
(16) León, A., et al., Monitorización de variables medioambientales y energéticas en la construcción de viviendas protegidas: Edificio Cros-Pirotecnia en Sevilla. Informes de la Construcción, 2010. 62(519): p. 67-82. https://doi.org/10.3989/ic.09.045
(17) International Energy Agency (IEA), E.A., Definition and Simulation of Occupant Behaviour in Buildings.
(18) Cuerda, E., M. Pérez, and J. Neila, Facade typologies as a tool for selecting refurbishment measures for the Spanish residential building stock. Energy and Buildings, 2014. 76: 119-129. https://doi.org/10.1016/j.enbuild.2014.02.054
(19) Drury B. Crawley, L.K.L., Energy Plus: creating a new-generation building energy simulation program. Energy and Buildings, 2001. 33: p. 319-331. https://doi.org/10.1016/S0378-7788(00)00114-6
(20) Aste, N., A. Angelotti, and M. Buzzetti, The influence of the external walls thermal inertia on the energy performance of well insulated buildings. Energy and Buildings, 2009. 41(11): p. 1181-1187. https://doi.org/10.1016/j.enbuild.2009.06.005
(21) Goldstein, D.B. and C. Eley, A classification of building energy performance indices. Energy Efficiency, 2014. 7(2): p. 353-375. https://doi.org/10.1007/s12053-013-9248-0
(22) Saari, A., et al., The effect of a redesigned floor plan, occupant density and the quality of indoor climate on the cost of space, productivity and sick leave in an office building–A case study. Building and Environment, 2006. 41(12): p. 1961-1972. https://doi.org/10.1016/j.buildenv.2005.07.012
(23) Chidiac, S., et al., Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings. Energy, 2011. 36(8): p. 5037-5052, https://doi.org/10.1016/j.energy.2011.05.050
(24) (INE), I.N.d.E., Características de los hogares. 2013, Ministerio de Economía y Competitividad.
(25) Hong, T., et al., An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 2015. 92: p. 764-777. https://doi.org/10.1016/j.buildenv.2015.02.019
(26) Edificación, C.T.d.l., DB HE Ahorro de energía, in Ministerio de Fomento y competitividad. 2013.
(27) Hong, T., et al., An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 2015. 92: p. 764-777. https://doi.org/10.1016/j.buildenv.2015.02.019
(28) Edificación, C.T.d.l. (2013). DB HE Ahorro de energía Ministerio de Fomento y competitividad.
(29) Stevenson, F. and A. Leaman, Evaluating housing performance in relation to human behaviour: new challenges. Building Research & Information, 2010. 38(5): 437-441. https://doi.org/10.1080/09613218.2010.497282
(30) Santin, O.G., Behavioural patterns and user profiles related to energy consumption for heating. Energy and Buildings, 2011. 43(10): p. 2662-2672. https://doi.org/10.1016/j.enbuild.2011.06.024
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Consejo Superior de Investigaciones Científicas (CSIC)
This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the print and online versions of this journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You may read the basic information and the legal text of the licence. The indication of the CC BY 4.0 licence must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the final version of the work produced by the publisher, is not allowed.
