Analysis of the Influence Subjective Human Parameters in the Calculation of Thermal Comfort and Energy Consumption of Buildings

[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.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.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/en12081531S123128Hemsath, 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.2014.11.044Griego, 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.2015.04.008Lin, 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.2013.05.040Pikas, 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.2014.01.039Terés-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.2014.10.018Lee, 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.2016.11.087Anderson, 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.2014.12.027Abdelaziz, 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.2010.09.003Nejat, 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.2014.11.066Balaras, 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.2004.08.003Pérez-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.2007.03.007Galvin, 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.2009.12.004Reducing 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-emissionsChappells †, 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:10.1080/0961321042000322762Geva, 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:10.1080/19401493.2013.819530Nguyen, 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:10.1080/19401493.2013.770067Rey 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:10.1080/23744731.2015.1056655Wardiningsih, 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:10.1080/00405000.2017.1398624Oropeza-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.2017.04.031Matzarakis, 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/s004840050119Peeters, 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.2008.07.011Ahmed, 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.2017.07.010Antoniadou, 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.2017.08.001Oropeza-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.2013.10.008Zhang, 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.2016.02.053Lei, 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.2015.10.031Chen, 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.25.5.1019Sivak, 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.2008.11.031Attia, 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:10.1080/19401493.2010.549573Crawley, 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)00114-6Newsham, 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)00009-1Schiavon, 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.2012.08.024Lee, 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.2013.11.020Kang, 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.2010.02.020Luo, 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.2013.12.057Manu, 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.2015.12.019Hwang, 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.2010.10.009Ioannou, 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.2015.01.055Hong, 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.2015.11.052Yan, 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.2015.08.032Putra, 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-xThomas, 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/s12273-017-0409-5Lindner, 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/s12273-017-0394-8Cedeno 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/s12273-017-0399-3Kuznik, 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.2010.01.012Salvalai, 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.2013.07.064Lebon, 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/s12053-016-9469-0Calleja 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.2013.08.009Basinska, 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.2015.08.029Tian, 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.2012.12.014Lomas, 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-dBreesch, 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.2010.05.008Peel, 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-4-439-2007Balvís, 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.2016.04.117Meteonorm: Irradiation Data for Every Place on Earth. Bern2014: Switzerlan https://meteonorm.comFanger, P. O. (1986). Thermal environment — Human requirements. The Environmentalist, 6(4), 275-278. doi:10.1007/bf02238059Hasan, 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.2016.10.007Ascione, 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.2016.04.078Méndez 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.2015.04.090Lam, 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)00031-3Havenith, 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)00008-7Nikolopoulou, 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)00093-1Humphreys, 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-xCoakley, 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.2014.05.00

Condition-dependent male copulatory courtship and its benefits for females

Postcopulatory sexual selection has shaped the ornaments used during copulatory courtship. However, we know relatively little about whether these courtship ornaments are costly to produce or whether they provide indirect benefits to females. We used the mealworm beetle, Tenebrio molitor, to explore this. We challenged males using an entomopathogenic fungus and compared their courtship (frequency of leg and antennal contacts to the female), copulation duration, number of eggs laid, and hatching rate against control males. Infected males copulated for longer yet they reduced their leg and antennal contacts compared to control males. However, there was no obvious relation between infection, copulation duration, and courtship with egg production and hatching success. In general, our results indicate that the ornaments used during postcopulatory courtship are condition-dependent. Moreover, such condition dependence cannot be linked to male fitness.Fil: Cargnelutti, Franco Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Reyes Ramírez, Alicia. Universidad Nacional Autónoma de México; MéxicoFil: Cristancho, Shara. Universidad El Bosque; ColombiaFil: Sandoval García, Iván A.. Universidad Nacional Autónoma de México; MéxicoFil: Rocha Ortega, Maya. Universidad Nacional Autónoma de México; MéxicoFil: Calbacho Rosa, Lucía Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Palacino, Freddy. Universidad El Bosque; ColombiaFil: Córdoba Aguilar, Alex. Universidad Nacional Autónoma de México; Méxic

Expedición Colombia BIO. Biodiversidad y conservación de los sistemas subterráneos y ambientes exocársticos asociados en El Peñón, Santander, Colombia

La primera Expedición de Biodiversidad Colombia BIO - Instituto Humboldt se llevó a cabo en el municipio del Peñón en Santander, con el objetivo de abordar la diversidad de fauna y flora en los ecosistemas subterráneos (endocársticos) y los ambientes superficiales (exocársticos) asociados a las cuevas.BogotáSubdirección de Investigacione

Elucidating the neuropathologic mechanisms of SARS-CoV-2 infection

Acknowledgements We want to express our gratitude to the Union Medical University Clinic, Dominican Republic, for their support and collaboration in the development of this research project. We also want to express our gratitude to the Mexican families who have donated the brain of their loved ones affected with Alzheimer's disease and made our research possible. This work is dedicated to the memory of Professor Dr. José Raúl Mena López†.Peer reviewedPublisher PD

Expediciones Humboldt: Honda-Méndez, Tolima

Este informe presenta los resultados de la caracterización biológica de uno de los bosques secos con mejor estado de conservación en el departamento del Tolima, ubicado entre los municipio de Honda, Méndez y Armero-Guayabal. Estos bosques se encuentran en una matriz de ganadería y producción agropecuaria, donde las coberturas boscosas son conservadas por los propietarios, conscientes de la importancia de este ecosistema para la provisión de bienes y servicios ecosistémicos. Esperamos que esta información producto de la capacidad científica del Instituto Humboldt, sea relevante y útil en las decisiones de planificación estratégica tanto en el ordenamiento territorial de los municipios de Honda, Méndez y Armero-Guayabal, como para las decisiones de conservación que se tomen en la regiónBogotáCiencias Básicas de la Biodiversida

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Biogeographical Survey Identifies Consistent Alternative Physiological Optima and a Minor Role for Environmental Drivers in Maintaining a Polymorphism

The contribution of adaptive mechanisms in maintaining genetic polymorphisms is still debated in many systems. To understand the contribution of selective factors in maintaining polymorphism, we investigated large-scale (>1000 km) geographic variation in morph frequencies and fitness-related physiological traits in the damselfly Nehalennia irene. As fitness-related physiological traits, we investigated investment in immune function (phenoloxidase activity), energy storage and fecundity (abdomen protein and lipid content), and flight muscles (thorax protein content). In the first part of the study, our aim was to identify selective agents maintaining the large-scale spatial variation in morph frequencies. Morph frequencies varied considerably among populations, but, in contrast to expectation, in a geographically unstructured way. Furthermore, frequencies co-varied only weakly with the numerous investigated ecological parameters. This suggests that spatial frequency patterns are driven by stochastic processes, or alternatively, are consequence of highly variable and currently unidentified ecological conditions. In line with this, the investigated ecological parameters did not affect the fitness-related physiological traits differently in both morphs. In the second part of the study, we aimed at identifying trade-offs between fitness-related physiological traits that may contribute to the local maintenance of both colour morphs by defining alternative phenotypic optima, and test the spatial consistency of such trade-off patterns. The female morph with higher levels of phenoloxidase activity had a lower thorax protein content, and vice versa, suggesting a trade-off between investments in immune function and in flight muscles. This physiological trade-off was consistent across the geographical scale studied and supports widespread correlational selection, possibly driven by male harassment, favouring alternative trait combinations in both female morphs

Expediciones Humboldt: San Francisco, Cundinamarca

Este informe presenta los resultados de la caracterización biológica de uno de los últimos grandes corredores ecológicos del territorio CAR, ubicado en el margen occidental del altiplano cundiboyacense. Este corredor, también conocido como el Espcarpe, incluye áreas prioritarias para la conservación de bosques alto andinos y páramos de la provincia de Gualivá y hace parte del Corredor de Conservación Bogotá-Región. Esperamos que esta información producto de la capacidad científica del Instituto Humboldt, sea relevante y útil en las decisiones de planificación estratégica tanto en el ordenamiento territorial de los municipios de San Francisco, Subachoque y Supatá, como para las decisiones de conservación de la Corporación Autónoma Regional.BogotáCiencias Básicas de la Biodiversida