Definiendo patrones de ocupación mediante la monitorización de edificios existentes

Autores/as

DOI:

https://doi.org/10.3989/id.53526

Palabras clave:

Comportamiento energético, patrones de ocupación, comportamiento de usuarios, monitorización de ocupación, evaluación post-ocupacional

Resumen


Para determinar el comportamiento energético de los edificios los programas de simulación dinámica son utilizados como métodos de cálculo. Sin embargo, numerosos estudios han mostrado que existen diferencias notables entre el comportamiento esperado y real de los edificios. Uno de los factores identificados como fuente de incertidumbre en la simulación de edificios es la ocupación y el comportamiento de los usuarios. Estos parámetros son definidos habitualmente con estándares que no reflejan la realidad de los ocupantes. En este artículo, se presenta una investigación sobre la influencia del comportamiento y la presencia de los usuarios de edificios residenciales en la demanda de energía. Para ello se generan modelos de simulación energética cuyos valores de entrada están ajustados con datos monitorizados de edificios reales. El estudio se realiza en dos casos de estudio ubicados en Madrid, España.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

(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

Publicado

2017-12-30

Cómo citar

Cuerda, E., Guerra-Santin, O., & Neila González, F. J. (2017). Definiendo patrones de ocupación mediante la monitorización de edificios existentes. Informes De La Construcción, 69(548), e223. https://doi.org/10.3989/id.53526

Número

Sección

Artículos