Tecnologías para la detección de ocupación en edificios
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ISSN: 2076-8133
E-ISSN: 2312-637X
Sección
Actualidad Tecnológica
Actualidad Tecnológica
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0.
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Enviado:
Jan 8, 2020
Publicado: Apr 20, 2020
Publicado: Apr 20, 2020
Resumen
Una importante parte del consumo de energía de los edificios está relacionado con el comportamiento de los ocupantes. Lo que significa que es necesario conocer con mayor precisión cómo se comporta y se mueve el usuario dentro de los ambientes, y este es uno de los retos que requiere conocer los tipos de tecnologías para la detección de la ocupación en edificios. En este artículo presentamos un resumen de las tecnologías utilizadas en la actualidad para el control de la ocupación en edificios, de manera de tener un marco de referencia y además poder indagar hacia donde se pueden dirigir las investigaciones futuras, tomando en cuenta el desarrollo actual.
Palabras clave
ocupación del edificio, monitoreo, rendimiento del edificio, sensoresDescargas
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Cómo citar
Mora, D., De Simone, M., & Chen Austin, M. (2020). Tecnologías para la detección de ocupación en edificios. Prisma Tecnológico, 11(1), 17-22. https://doi.org/10.33412/pri.v11.1.2530
Citas
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(2) (2009) Green Globes, The green building initiative. [Online] Disponible en: http://www.greenglobes.com/home.asp.
(3) D. Mora, C. Carpino, M. De Simone, Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions., Energy Efficiency. 2017. doi:10.1007/s12053-017-9553-0.
(4) (2015) IEA, IEA-EBC Annex 66: Definition and simulation of occupant behavior in buildings. [Online] Disponible en: www.annex66.org.
(5) V. Fabi, R.V. Andersen, S. Corgnati, B.W. Olesen, Occupants’ window opening behaviour: a literature review of factors influencing occupant behaviour and models, Building and Environment. 2012. vol. 58, 188–198. doi:10.1016/j.buildenv.2012.07.009.
(6) A.F. Emery, C.J. Kippenhan, A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards, Energy. 2006, vol. 31, 677–693. doi:10.1016/j.energy.2005.04.006.
(7) O. Guerra-Santin, Actual energy consumption in dwellings -the effect of energy performance regulations and occupant behaviour, IOS Press under the imprint Delft University Press, 2010.
(8) S. Chen, W. Yang, H. Yoshino, M.D. Levine, K. Newhouse, A. Hinge, Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies, Energy and Buildings. 2015. vol. 104, 1–13. doi:10.1016/j.enbuild.2015.06.075.
(9) Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energy and Buildings. 2011. vol. 43, 1409–1417.
(10) R. Yang, L. Wang, Development of multi-agent system for building energy and comfort management based on occupant behaviors, Energy and Buildings. 2013. vol. 53, 1–7. doi:10.1016/j.enbuild.2012.10.025.
(11) K. Christensen, R. Melfi, B. Nordman, B. Rosenblum, R. Viera, Using existing network infrastructure to estimate building occupancy and control plugged-in devices in user workspaces, International Journal Communication Networks and Distributed Systems. 2014. vol. 12, 4–29.
(12) M.B. Kjærgaard, F.C. Sangogboye, Categorization framework and survey of occupancy sensing systems, Pervasive and Mobile Computing. 38 (2017) 1–13. doi:10.1016/j.pmcj.2016.09.019.
(13) R. Zhang, K.P. Lam, Y.-S. Chiou, B. Dong, Information-theoretic environment features selection for occupancy detection in open office spaces, Building Simulation. 2012. vol. 5, 179–188. doi:10.1007/s12273-012-0075-6.
(14) T. Labeodan, W. Zeiler, G. Boxem, Y. Zhao, Occupancy measurement in commercial office buildings for demand-driven control applications - A survey and detection system evaluation, Energy and Buildings. 93 (2015) 303–314. doi:10.1016/j.enbuild.2015.02.028.
(15) T. Teixeira, G. Dublon, A. Savvides, A survey of human-sensing: methods for detecting presence, count, location, track, and identity, ACM Computing Surveys. 2010. vol. 5, 1–35. http://www.eng.yale.edu/enalab/publications/human_sensing_enalabWIP.pdf.
(16) C. Martani, D. Lee, P. Robinson, R. Britter, C. Ratti, ENERNET: Studying the dynamic relationship between building occupancy and energy consumption, Energy and Buildings. 2012. vol. 47, 584–591. doi:10.1016/j.enbuild.2011.12.037.
(17) F. Jazizadeh, W. Jung, Personalized thermal comfort inference using RGB video images for distributed HVAC control, Applied Energy. 2018. vol. 220, 829–841. doi:10.1016/j.apenergy.2018.02.049.
(18) R. Melfi, B. Rosenblum, B. Nordman, K. Christensen, Measuring building occupancy using existing network infrastructure, in: International Green Computing Conference, 2011.
(19) M. Kapsalaki, V. Leal, Recent progress on net zero energy buildings, Advances in Building Energy Research. 2011. vol. 5, 129–162. doi:10.1080/17512549.2011.582352.
(20) I. Sartori, A. Napolitano, K. Voss, Net zero energy buildings: a consistent definition framework, Energy and Buildings. 2012. vol. 48, 220–232. doi:10.1016/j.enbuild.2012.01.032.
(21) S. Pigg, M. Eilers, J. Reed, Behavioral Aspects of Lighting and Occupancy Sensors in Privates Offices: A case study of a University Office Building, in: ACEEE 1996 Summer Study on Energy Efficiency in Buildings, 1996: pp. 161–170.
(22) B. Dong, M.B. Kjærgaard, M. De Simone, H.B. Gunay, W. O’brien, D. Mora, J. Dziedzic, J. Zhao, Sensing and Data Acquisition, in: A. Wagner, W. O’brien, B. Dong (Eds.), Exploring Occupant Behavior in Buildings- Methods and Challenges, Springer International Publishing AG, 2018. doi:10.1007/978-3-319-61464-9.
(23) D. Liu, X. Guan, Y. Du, Q. Zhao, Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors, Measurement Science and Technology. 2013. vol. 24, 074023. doi:10.1088/0957-0233/24/7/074023.
(24) D. Mora, G. Fajilla, M.C. Austin, M. De Simone, Occupancy patterns obtained by heuristic approaches: cluster analysis and logical flowcharts. A case study in a university office, Energy and Buildings. 2019. doi:10.1016/j.enbuild.2019.01.023.
(25) B. Dong, K.P. Lam, Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network, Journal of Building Performance Simulation. 2011. vol. 4, 359–369. doi:10.1080/19401493.2011.577810.
(26) A. Ebadat, G. Bottegal, D. Varagnolo, B. Wahlberg, K.H. Johansson, Estimation of building occupancy levels through environmental signals deconvolution, in: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings - BuildSys’13, 2013: pp. 1–8. doi:10.1145/2528282.2528290.
(27) J. Yang, M. Santamouris, S.E. Lee, Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings, Energy and Buildings. 2015. vol. 121 1–6. doi:10.1016/j.enbuild.2015.12.019.
(28) C. Carpino, D. Mora, M. De Simone, On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives, Energy and Buildings. 2019. doi:10.1016/j.enbuild.2018.12.021.
(29) W. Wang, J. Chen, T. Hong, Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings, Automation in Construction. 2018. vol. 94, 233–243. doi:10.1016/j.autcon.2018.07.007.
(30) Z. Chen, C. Jiang, L. Xie, Building occupancy estimation and detection: A review, Energy and Buildings. 2018. vol. 169, 260–270. doi:10.1016/j.enbuild.2018.03.084.