Mostrar el registro sencillo del ítem
dc.contributor.author | Robledo-Fava, Roberto | es_ES |
dc.contributor.author | Hernández-Luna, Monica C. | es_ES |
dc.contributor.author | Fernández de Córdoba, Pedro | es_ES |
dc.contributor.author | Michinel, Humberto | es_ES |
dc.contributor.author | Zaragoza, Sonia | es_ES |
dc.contributor.author | Castillo-Guzman, A. | es_ES |
dc.contributor.author | Selvas-Aguilar, Romeo | es_ES |
dc.date.accessioned | 2020-12-02T04:31:12Z | |
dc.date.available | 2020-12-02T04:31:12Z | |
dc.date.issued | 2019-04-02 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/156257 | |
dc.description.abstract | [EN] In the present work, we analyze the influence of the designer's choice of values for the human metabolic index (met) and insulation by clothing (clo) that can be selected within the ISO 7730 for the calculation of the energy demand of buildings. To this aim, we first numerically modeled, using TRNSYS, two buildings in different countries and climatologies. Then, we consistently validated our simulations by predicting indoor temperatures and comparing them with measured data. After that, the energy demand of both buildings was obtained. Subsequently, the variability of the set-point temperature concerning the choice of clo and met, within limits prescribed in ISO 7730, was analyzed using a Monte Carlo method. This variability of the interior comfort conditions has been finally used in the numerical model previously validated, to calculate the changes in the energy demand of the two buildings. Therefore, this work demonstrated that the diversity of possibilities offered by ISO 7730 for the choice of clo and met results, depending on the values chosen by the designer, in significant differences in indoor comfort conditions, leading to non-negligible changes in the calculations of energy consumption, especially in the case of big buildings. | es_ES |
dc.description.sponsorship | This work was partially funded by grants OHMERA MAT2017-86453-R, FIS2017-83762-P and ENE2015-71333-R from MINECO (Spain). R. Robledo and M. Hernandez were supported by CONACYT grants 298503 and 296471, respectively. We also thanks to supporting given by the project number INFRA-187906 from the Mexican National Council of Science and Technology-CONACYT. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Energies | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Monte Carlo method | es_ES |
dc.subject | ISO 7730 | es_ES |
dc.subject | TRNSYS | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.title | Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/en12081531 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MAT2017-86453-R/ES/MATERIALES HIBRIDOS ORGANICO-INORGANICOS PARA APLICACIONES DE REFRIGERACION ECOLOGICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FIS2017-83762-P/ES/SIMULACION OPTICA DE MATERIA OSCURA Y OTROS SISTEMAS DE FISICA FUNDAMENTAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CONACyT//298503/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CONACyT//296471/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CONACyT//INFRA-187906/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//ENE2015-71333-R/ES/CONVECCION FORZADA EN CANALES TURBULENTOS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Robledo-Fava, R.; Hernández-Luna, MC.; Fernández De Córdoba, P.; Michinel, H.; Zaragoza, S.; Castillo-Guzman, A.; Selvas-Aguilar, R. (2019). Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings. Energies. 12(8):1-23. https://doi.org/10.3390/en12081531 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/en12081531 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 23 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.description.issue | 8 | es_ES |
dc.identifier.eissn | 1996-1073 | es_ES |
dc.relation.pasarela | S\384896 | es_ES |
dc.contributor.funder | Ministerio de Economía y Empresa | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Consejo Nacional de Ciencia y Tecnología, México | es_ES |
dc.description.references | Hemsath, T. L., & Alagheband Bandhosseini, K. (2015). Sensitivity analysis evaluating basic building geometry’s effect on energy use. Renewable Energy, 76, 526-538. doi:10.1016/j.renene.2014.11.044 | es_ES |
dc.description.references | Griego, D., Krarti, M., & Hernandez-Guerrero, A. (2015). Energy efficiency optimization of new and existing office buildings in Guanajuato, Mexico. Sustainable Cities and Society, 17, 132-140. doi:10.1016/j.scs.2015.04.008 | es_ES |
dc.description.references | Lin, H.-W., & Hong, T. (2013). On variations of space-heating energy use in office buildings. Applied Energy, 111, 515-528. doi:10.1016/j.apenergy.2013.05.040 | es_ES |
dc.description.references | Pikas, E., Thalfeldt, M., & Kurnitski, J. (2014). Cost optimal and nearly zero energy building solutions for office buildings. Energy and Buildings, 74, 30-42. doi:10.1016/j.enbuild.2014.01.039 | es_ES |
dc.description.references | Terés-Zubiaga, J., Campos-Celador, A., González-Pino, I., & Escudero-Revilla, C. (2015). Energy and economic assessment of the envelope retrofitting in residential buildings in Northern Spain. Energy and Buildings, 86, 194-202. doi:10.1016/j.enbuild.2014.10.018 | es_ES |
dc.description.references | Lee, J., Kim, J., Song, D., Kim, J., & Jang, C. (2017). Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons. Renewable and Sustainable Energy Reviews, 75, 1081-1088. doi:10.1016/j.rser.2016.11.087 | es_ES |
dc.description.references | Anderson, J. E., Wulfhorst, G., & Lang, W. (2015). Energy analysis of the built environment—A review and outlook. Renewable and Sustainable Energy Reviews, 44, 149-158. doi:10.1016/j.rser.2014.12.027 | es_ES |
dc.description.references | Abdelaziz, E. A., Saidur, R., & Mekhilef, S. (2011). A review on energy saving strategies in industrial sector. Renewable and Sustainable Energy Reviews, 15(1), 150-168. doi:10.1016/j.rser.2010.09.003 | es_ES |
dc.description.references | Nejat, P., Jomehzadeh, F., Taheri, M. M., Gohari, M., & Abd. Majid, M. Z. (2015). A global review of energy consumption, CO 2 emissions and policy in the residential sector (with an overview of the top ten CO 2 emitting countries). Renewable and Sustainable Energy Reviews, 43, 843-862. doi:10.1016/j.rser.2014.11.066 | es_ES |
dc.description.references | Balaras, C. A., Droutsa, K., Dascalaki, E., & Kontoyiannidis, S. (2005). Heating energy consumption and resulting environmental impact of European apartment buildings. Energy and Buildings, 37(5), 429-442. doi:10.1016/j.enbuild.2004.08.003 | es_ES |
dc.description.references | Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394-398. doi:10.1016/j.enbuild.2007.03.007 | es_ES |
dc.description.references | Galvin, R. (2010). Thermal upgrades of existing homes in Germany: The building code, subsidies, and economic efficiency. Energy and Buildings, 42(6), 834-844. doi:10.1016/j.enbuild.2009.12.004 | es_ES |
dc.description.references | Reducing US Greenhouse Gas Emissions: How Much at What Cost?: US Greenhouse Gas Abatement Mapping Initiative https://www.mckinsey.com/business-functions/sustainability/our-insights/reducing-us-greenhouse-gas-emissions | es_ES |
dc.description.references | Chappells †, H., & Shove ‡, E. (2005). Debating the future of comfort: environmental sustainability, energy consumption and the indoor environment. Building Research & Information, 33(1), 32-40. doi:10.1080/0961321042000322762 | es_ES |
dc.description.references | Geva, A., Saaroni, H., & Morris, J. (2013). Measurements and simulations of thermal comfort: a synagogue in Tel Aviv, Israel. Journal of Building Performance Simulation, 7(3), 233-250. doi:10.1080/19401493.2013.819530 | es_ES |
dc.description.references | Nguyen, A. T., & Reiter, S. (2013). Passive designs and strategies for low-cost housing using simulation-based optimization and different thermal comfort criteria. Journal of Building Performance Simulation, 7(1), 68-81. doi:10.1080/19401493.2013.770067 | es_ES |
dc.description.references | Rey Martínez, F. J., Chicote, M. A., Peñalver, A. V., Gónzalez, A. T., & Gómez, E. V. (2015). Indoor air quality and thermal comfort evaluation in a Spanish modern low-energy office with thermally activated building systems. Science and Technology for the Built Environment, 21(8), 1091-1099. doi:10.1080/23744731.2015.1056655 | es_ES |
dc.description.references | Wardiningsih, W., & Troynikov, O. (2017). Force attenuation capacity and thermophysiological wear comfort of vertically lapped nonwoven fabric. The Journal of The Textile Institute, 109(8), 1035-1043. doi:10.1080/00405000.2017.1398624 | es_ES |
dc.description.references | Oropeza-Perez, I., Petzold-Rodriguez, A. H., & Bonilla-Lopez, C. (2017). Adaptive thermal comfort in the main Mexican climate conditions with and without passive cooling. Energy and Buildings, 145, 251-258. doi:10.1016/j.enbuild.2017.04.031 | es_ES |
dc.description.references | Matzarakis, A., Mayer, H., & Iziomon, M. G. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43(2), 76-84. doi:10.1007/s004840050119 | es_ES |
dc.description.references | Peeters, L., Dear, R. de, Hensen, J., & D’haeseleer, W. (2009). Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Applied Energy, 86(5), 772-780. doi:10.1016/j.apenergy.2008.07.011 | es_ES |
dc.description.references | Ahmed, K., Akhondzada, A., Kurnitski, J., & Olesen, B. (2017). Occupancy schedules for energy simulation in new prEN16798-1 and ISO/FDIS 17772-1 standards. Sustainable Cities and Society, 35, 134-144. doi:10.1016/j.scs.2017.07.010 | es_ES |
dc.description.references | Antoniadou, P., & Papadopoulos, A. M. (2017). Occupants’ thermal comfort: State of the art and the prospects of personalized assessment in office buildings. Energy and Buildings, 153, 136-149. doi:10.1016/j.enbuild.2017.08.001 | es_ES |
dc.description.references | Oropeza-Perez, I., & Østergaard, P. A. (2014). Potential of natural ventilation in temperate countries – A case study of Denmark. Applied Energy, 114, 520-530. doi:10.1016/j.apenergy.2013.10.008 | es_ES |
dc.description.references | Zhang, L., Zhang, L., & Wang, Y. (2016). Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Solar Energy, 132, 38-50. doi:10.1016/j.solener.2016.02.053 | es_ES |
dc.description.references | Lei, J., Yang, J., & Yang, E.-H. (2016). Energy performance of building envelopes integrated with phase change materials for cooling load reduction in tropical Singapore. Applied Energy, 162, 207-217. doi:10.1016/j.apenergy.2015.10.031 | es_ES |
dc.description.references | Chen, C.-W., Lee, C.-W., & Lin, Y.-W. (2014). Air Conditioning — Optimizing Performance by Reducing Energy Consumption. Energy & Environment, 25(5), 1019-1024. doi:10.1260/0958-305x.25.5.1019 | es_ES |
dc.description.references | Sivak, M. (2009). Potential energy demand for cooling in the 50 largest metropolitan areas of the world: Implications for developing countries. Energy Policy, 37(4), 1382-1384. doi:10.1016/j.enpol.2008.11.031 | es_ES |
dc.description.references | Attia, S., Hensen, J. L. M., Beltrán, L., & De Herde, A. (2012). Selection criteria for building performance simulation tools: contrasting architects’ and engineers’ needs. Journal of Building Performance Simulation, 5(3), 155-169. doi:10.1080/19401493.2010.549573 | es_ES |
dc.description.references | Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., … Glazer, J. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33(4), 319-331. doi:10.1016/s0378-7788(00)00114-6 | es_ES |
dc.description.references | Newsham, G. R. (1997). Clothing as a thermal comfort moderator and the effect on energy consumption. Energy and Buildings, 26(3), 283-291. doi:10.1016/s0378-7788(97)00009-1 | es_ES |
dc.description.references | Schiavon, S., & Lee, K. H. (2013). Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Building and Environment, 59, 250-260. doi:10.1016/j.buildenv.2012.08.024 | es_ES |
dc.description.references | Lee, Y. S., & Malkawi, A. M. (2014). Simulating multiple occupant behaviors in buildings: An agent-based modeling approach. Energy and Buildings, 69, 407-416. doi:10.1016/j.enbuild.2013.11.020 | es_ES |
dc.description.references | Kang, D. H., Mo, P. H., Choi, D. H., Song, S. Y., Yeo, M. S., & Kim, K. W. (2010). Effect of MRT variation on the energy consumption in a PMV-controlled office. Building and Environment, 45(9), 1914-1922. doi:10.1016/j.buildenv.2010.02.020 | es_ES |
dc.description.references | Luo, M., Cao, B., Zhou, X., Li, M., Zhang, J., Ouyang, Q., & Zhu, Y. (2014). Can personal control influence human thermal comfort? A field study in residential buildings in China in winter. Energy and Buildings, 72, 411-418. doi:10.1016/j.enbuild.2013.12.057 | es_ES |
dc.description.references | Manu, S., Shukla, Y., Rawal, R., Thomas, L. E., & de Dear, R. (2016). Field studies of thermal comfort across multiple climate zones for the subcontinent: India Model for Adaptive Comfort (IMAC). Building and Environment, 98, 55-70. doi:10.1016/j.buildenv.2015.12.019 | es_ES |
dc.description.references | Hwang, R.-L., & Shu, S.-Y. (2011). Building envelope regulations on thermal comfort in glass facade buildings and energy-saving potential for PMV-based comfort control. Building and Environment, 46(4), 824-834. doi:10.1016/j.buildenv.2010.10.009 | es_ES |
dc.description.references | Ioannou, A., & Itard, L. C. M. (2015). Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy. Energy and Buildings, 92, 216-233. doi:10.1016/j.enbuild.2015.01.055 | es_ES |
dc.description.references | Hong, T., Taylor-Lange, S. C., D’Oca, S., Yan, D., & Corgnati, S. P. (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116, 694-702. doi:10.1016/j.enbuild.2015.11.052 | es_ES |
dc.description.references | Yan, D., O’Brien, W., Hong, T., Feng, X., Burak Gunay, H., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264-278. doi:10.1016/j.enbuild.2015.08.032 | es_ES |
dc.description.references | Putra, H. C., Andrews, C. J., & Senick, J. A. (2017). An agent-based model of building occupant behavior during load shedding. Building Simulation, 10(6), 845-859. doi:10.1007/s12273-017-0384-x | es_ES |
dc.description.references | Thomas, A., Menassa, C. C., & Kamat, V. R. (2017). Lightweight and adaptive building simulation (LABS) framework for integrated building energy and thermal comfort analysis. Building Simulation, 10(6), 1023-1044. doi:10.1007/s12273-017-0409-5 | es_ES |
dc.description.references | Lindner, A. J. M., Park, S., & Mitterhofer, M. (2017). Determination of requirements on occupant behavior models for the use in building performance simulations. Building Simulation, 10(6), 861-874. doi:10.1007/s12273-017-0394-8 | es_ES |
dc.description.references | Cedeno Laurent, J. G., Samuelson, H. W., & Chen, Y. (2017). The impact of window opening and other occupant behavior on simulated energy performance in residence halls. Building Simulation, 10(6), 963-976. doi:10.1007/s12273-017-0399-3 | es_ES |
dc.description.references | Kuznik, F., Virgone, J., & Johannes, K. (2010). Development and validation of a new TRNSYS type for the simulation of external building walls containing PCM. Energy and Buildings, 42(7), 1004-1009. doi:10.1016/j.enbuild.2010.01.012 | es_ES |
dc.description.references | Salvalai, G., Pfafferott, J., & Sesana, M. M. (2013). Assessing energy and thermal comfort of different low-energy cooling concepts for non-residential buildings. Energy Conversion and Management, 76, 332-341. doi:10.1016/j.enconman.2013.07.064 | es_ES |
dc.description.references | Lebon, M., Fellouah, H., Galanis, N., Limane, A., & Guerfala, N. (2016). Numerical analysis and field measurements of the airflow patterns and thermal comfort in an indoor swimming pool: a case study. Energy Efficiency, 10(3), 527-548. doi:10.1007/s12053-016-9469-0 | es_ES |
dc.description.references | Calleja Rodríguez, G., Carrillo Andrés, A., Domínguez Muñoz, F., Cejudo López, J. M., & Zhang, Y. (2013). Uncertainties and sensitivity analysis in building energy simulation using macroparameters. Energy and Buildings, 67, 79-87. doi:10.1016/j.enbuild.2013.08.009 | es_ES |
dc.description.references | Basinska, M., Koczyk, H., & Szczechowiak, E. (2015). Sensitivity analysis in determining the optimum energy for residential buildings in Polish conditions. Energy and Buildings, 107, 307-318. doi:10.1016/j.enbuild.2015.08.029 | es_ES |
dc.description.references | Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20, 411-419. doi:10.1016/j.rser.2012.12.014 | es_ES |
dc.description.references | Lomas, K. J., & Eppel, H. (1992). Sensitivity analysis techniques for building thermal simulation programs. Energy and Buildings, 19(1), 21-44. doi:10.1016/0378-7788(92)90033-d | es_ES |
dc.description.references | Breesch, H., & Janssens, A. (2010). Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis. Solar Energy, 84(8), 1453-1467. doi:10.1016/j.solener.2010.05.008 | es_ES |
dc.description.references | Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439-473. doi:10.5194/hessd-4-439-2007 | es_ES |
dc.description.references | Balvís, E., Sampedro, Ó., Zaragoza, S., Paredes, A., & Michinel, H. (2016). A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings. Applied Energy, 177, 60-70. doi:10.1016/j.apenergy.2016.04.117 | es_ES |
dc.description.references | Meteonorm: Irradiation Data for Every Place on Earth. Bern2014: Switzerlan https://meteonorm.com | es_ES |
dc.description.references | Fanger, P. O. (1986). Thermal environment — Human requirements. The Environmentalist, 6(4), 275-278. doi:10.1007/bf02238059 | es_ES |
dc.description.references | Hasan, M. H., Alsaleem, F., & Rafaie, M. (2016). Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation. Building and Environment, 110, 173-183. doi:10.1016/j.buildenv.2016.10.007 | es_ES |
dc.description.references | Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P. (2016). Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality. Applied Energy, 174, 37-68. doi:10.1016/j.apenergy.2016.04.078 | es_ES |
dc.description.references | Méndez Echenagucia, T., Capozzoli, A., Cascone, Y., & Sassone, M. (2015). The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 154, 577-591. doi:10.1016/j.apenergy.2015.04.090 | es_ES |
dc.description.references | Lam, J. C., & Hui, S. C. M. (1996). Sensitivity analysis of energy performance of office buildings. Building and Environment, 31(1), 27-39. doi:10.1016/0360-1323(95)00031-3 | es_ES |
dc.description.references | Havenith, G., Holmér, I., & Parsons, K. (2002). Personal factors in thermal comfort assessment: clothing properties and metabolic heat production. Energy and Buildings, 34(6), 581-591. doi:10.1016/s0378-7788(02)00008-7 | es_ES |
dc.description.references | Nikolopoulou, M., Baker, N., & Steemers, K. (2001). Thermal comfort in outdoor urban spaces: understanding the human parameter. Solar Energy, 70(3), 227-235. doi:10.1016/s0038-092x(00)00093-1 | es_ES |
dc.description.references | Humphreys, M. A., & Fergus Nicol, J. (2002). The validity of ISO-PMV for predicting comfort votes in every-day thermal environments. Energy and Buildings, 34(6), 667-684. doi:10.1016/s0378-7788(02)00018-x | es_ES |
dc.description.references | Coakley, D., Raftery, P., & Keane, M. (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews, 37, 123-141. doi:10.1016/j.rser.2014.05.007 | es_ES |