Relación entre parámetros constructivos y cargas térmicas en un edificio sin ganancias internas
DOI:
https://doi.org/10.17533/udea.redin.20210955Palabras clave:
Eficiencia energética en edificios, análisis de sensibilidad, evaluación constructiva multivariable, cargas térmicasResumen
El análisis de las características de un edificio indica que hay algunas incertidumbres que influyen en su rendimiento energético: clima, volumetría o condiciones de funcionamiento. Es importante contar con un sistema asequible que realice este análisis y la gestión energética optimizando el acoplamiento entre producción y consumo. Conocer la relación entre las necesidades térmicas y diferentes parámetros constructivos puede ayudar a definir este sistema, permitiendo comprender el consumo previsto de calefacción y refrigeración en base a información fácilmente disponible. En este trabajo se ha aplicado una metodología numérica para estimar las cargas térmicas de un edificio sin ganancias internas. Para ello se ha desarrollado un entorno de simulación con el que ejecutar un análisis de sensibilidad, acoplando los programas TRNSYS 16.1 y GenOpt, para evaluar diversas variables de análisis del estudio paramétrico: Volumetría, materiales de construcción según normativas españolas y porcentaje de ventanas exteriores. Se han calculado las cargas de calefacción y refrigeración para cuantificar su influencia: Las normativas más antiguas implican cargas anuales más elevadas; mayor altura y superficie del edificio reduce las cargas, y mayores porcentajes de ventanas en las fachadas implican mayores demandas, particularmente en orientaciones Este y Oeste. La variación de la envolvente resulta el factor más influyente. Finalmente se ha realizado un estudio estadístico para evaluar las tendencias anuales. En calefacción muestran mayor estabilidad con dos intervalos definidos, mientras en refrigeración se observa más asimetría.
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U. Nations. (2016) The world’s cities in 2016 : data booklet. New York. [Online]. Available: https://digitallibrary.un.org/record/1634928?ln=es
BP. (2016, Mar. 4,) Bp annual report and form 20-f. [Online]. Available: https://on.bp.com/3yjiwZF
A. Asikainen, E. Pärjälä, M. Jantunen, J. Tuomisto, and C. E. Sabel, “Effects of local greenhouse gas abatement strategies on air pollutant emissions and on health in kuopio, finland,” Climate, vol. 5, no. 2, Jun. 19, 2017. [Online]. Available: https://doi.org/10.3390/cli5020043
Energy efficient buildings. European Comission. Accessed Ene. 25, 2018. [Online]. Available: https://bit.ly/2S72Ojr
E. performance of buildings directive, “Directive 2010/31/eu of the european parliament and of the council of 19 may 2010 on the energy performance of buildings (recast),” Official Journal of the European Union, Jun. 18, 2010. [Online]. Available: https://bit.ly/3whhN9C
S. Hallegatte, “Strategies to adapt to an uncertain climate change,” Global Environmental Change, vol. 19, no. 2, May. 2009. [Online]. Available: https://doi.org/10.1016/j.gloenvcha.2008.12.003
F. Ascione, N. Bianco, G. M. Mauro, and D. F. N. snd G. P. Vanoli, “A multi-criteria approach to achieve constrained cost-optimal energy retrofits of buildings by mitigating climate change and urban overheating,” Climate, vol. 6, no. 2, May. 8, 2018. [Online]. Available: https://doi.org/10.3390/cli6020037
M. A. R. Biswas, M. D. Robinson, and N. Fumo, “Prediction of residential building energy consumption: A neural network approach,” Energy, vol. 117, no. Parte 1, Dic. 15, 2016. [Online]. Available: https://doi.org/10.1016/j.energy.2016.10.066
E. Giancola, C. I. M. Chicott, and M. R. H. Celemin, “Desarrollo de un modelo E-GIS DB (environment and energy geographical information system database) para apoyar iniciativas de ciudades inteligentes,” Presentado en el I Congreso Ciudades Inteligentes, Madrid, España, 2015.
D. B. Crawley, J. W. Hand, M. Kummert, and B. T. Griffith, “Contrasting the capabilities of building energy performance simulation programs,” Building and Environment, vol. 43, no. 4, Abr. 2008. [Online]. Available: https://doi.org/10.1016/j.buildenv.2006.10.027
S. Soutullo and et al., “Energy performance assessment of a polygeneration plant in different weather conditions through simulation tools,” Energy and Buildings, vol. 124, Jul. 15, 2016. [Online]. Available: https://doi.org/10.1016/j.enbuild.2016.04.031
R. Zhang, P. A. Mirzaei, and B. Jones, “Development of a dynamic external CFD and BES coupling framework for application of urban neighbourhoods energy modelling,” Building and Environment, vol. 146, Dic. 2018. [Online]. Available: https://doi.org/10.1016/j.buildenv.2018.09.006
L. Tronchin, M. Manfren, and L. C. Tagliabue, “Optimization of building energy performance by means of multi-scale analysis – lessons learned from case studies,” Sustainable Cities and Society, vol. 27, Nov. 2016. [Online]. Available: https://doi.org/10.1016/j.scs.2015.11.003
R. Enríquez, M. J. Jiménez, and M. R. Heras, “Towards non-intrusive thermal load monitoring of buildings: BES calibration,” Applied Energy, vol. 191, Abr. 1, 2017. [Online]. Available: https://doi.org/10.1016/j.apenergy.2017.01.050
S. Soutullo, E. Giancola, and M. R. Heras, “Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in madrid,” Energy, vol. 152, Jun. 1, 2018. [Online]. Available: https://doi.org/10.1016/j.energy.2018.02.017
E. Asadi, M. G. da Silva, C. H. Antunes, L. Dias, and L. Glicksman, “Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application,” Energy and Buildings, vol. 81, Oct. 2014. [Online]. Available: https://doi.org/10.1016/j.enbuild.2014.06.009
A. T. Nguyen, S. Reiter, and P. Rigo, “A review on simulation-based optimization methods applied to building performance analysis,” Applied Energy, vol. 113, Ene. 2014. [Online]. Available: https://doi.org/10.1016/j.apenergy.2013.08.061
R. Baños and et al., “Optimization methods applied to renewable and sustainable energy: A review,” Renewable and Sustainable Energy Reviews, vol. 15, no. 4, May. 2011. [Online]. Available: https://doi.org/10.1016/j.rser.2010.12.008
J. Wright and A. Alajmi, “Efficient genetic algorithm sets for optimizing constrained building design problem,” International Journal of Sustainable Built Environment, vol. 5, no. 1, Jun. 2016. [Online]. Available: https://doi.org/10.1016/j.ijsbe.2016.04.001
GENOPT webpage, Lawrence Berkeley National Laboratory, accessed Sep. 11, 2020.
TRNSYS webpage, TRNSYS, accessed Sep. 11, 2020.
A. Alajmi and J. Wright, “The robustness of genetic algorithms in solving unconstrained building optimization problems,” presented in Proceedings of the 9th International Building Performance Simulation Association Conference, Canada, 2005.
D. Tuhus-Dubrow and M. Krarti, “Genetic-algorithm based approach to optimize building envelope design for residential buildings,” Building and Environment, vol. 45, no. 7, Jul. 2010. [Online]. Available: https://doi.org/10.1016/j.buildenv.2010.01.005
A. Hasan, M. Vuolle, and K. Sirén, “Minimisation of life cycle cost of a detached house using combined simulation and optimisation,” Building and Environment, vol. 43, no. 12, Dic. 2008. [Online]. Available: https://doi.org/10.1016/j.buildenv.2007.12.003
B. Eisenhower, Z. O’Neill, S. Narayanan, V. A. Fonoberov, and I. Mezić, “A methodology for meta-model based optimization in building energy models,” Energy and Buildings, vol. 47, Abr. 2012. [Online]. Available: https://doi.org/10.1016/j.enbuild.2011.12.001
S. Soutullo, E. Giancola, M. J. Jiménez, J. A. Ferrer, and M. N. Sánchez, “How climate trends impact on the thermal performance of a typical residential building in madrid,” Energies, vol. 13, no. 1, Ene. 3, 2020. [Online]. Available: https://doi.org/10.3390/en13010237
P. van den Brom, A. R. Hansen, K. Gram-Hanssen, A. Meijer, and H. Visscher, “Variances in residential heating consumption - importance of building characteristics and occupants analysed by movers and stayers,” Applied Energy, vol. 250, Sep. 15, 2019. [Online]. Available: https://doi.org/10.1016/j.apenergy.2019.05.078
Sisgener. CARTIF. Accessed Sep. 11, 2020. [Online]. Available: https://www.cartif.es/sisgener/
J. A. Díaz and et al., “Modelo reducido de predicción de demanda de edificios residenciales en base a parámetros meteorológicos,” in CIES2020: As Energias Renováveis na Transição Energética: Livro de Comunicações do XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar, H. Gonçalves and M. Romero, Eds., Lisboa, Portugal, 2020, pp. 1061–1068.
X. Li and et al., “Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings,” Applied Energy, vol. 258, Ene. 15, 2020. [Online]. Available: https://doi.org/10.1016/j.apenergy.2019.114021
J. S. Hygh, J. F. D. Carolis, D. B. Hill, and S. R. Ranjithan, “Multivariate regression as an energy assessment tool in early building design,” Building and Environment, vol. 57, Nov. 2012. [Online]. Available: https://doi.org/10.1016/j.buildenv.2012.04.021
C. J. Hopfe and J. L. M. Hensen, “Uncertainty analysis in building performance simulation for design support,” Energy and Buildings, vol. 43, no. 10, Oct. 2011. [Online]. Available: https://doi.org/10.1016/j.enbuild.2011.06.034
F. S. Westphal and R. Lamberts, “Building simulation calibration using sensitivity analysis,” in Ninth International IBPSA Conference, Canada, 2005, pp. 1331–1338.
J. Sun and T. A. Raddy, “Calibration of building energy simulation programs using the analytic optimization approach (rp-1051),” HVAC&R Research, vol. 12, no. 1, 2006. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/10789669.2006.10391173
J. C. Lam, K. K. W. Wan, and L. Yang, “Sensitivity analysis and energy conservation measures implications,” Energy Conversion and Management, vol. 49, no. 11, Nov. 2008. [Online]. Available: https://doi.org/10.1016/j.enconman.2008.05.022
W. Tian and R. Choudhary, “A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater london,” Energy and Buildings, vol. 54, Nov. 2012. [Online]. Available: https://doi.org/10.1016/j.enbuild.2012.06.031
W. Tian and P. de Wilde, “Uncertainty and sensitivity analysis of building performance using probabilistic climate projections: A UK case study,” Automation in Construction, vol. 20, no. 8, Dic. 2011. [Online]. Available: https://doi.org/10.1016/j.autcon.2011.04.011
A. Saltelli, M. Ratto, S. Tarantola, and F. Campolongo, “Update 1 of: Sensitivity analysis for chemical models,” Chemical Reviews, vol. 112, no. 5, Abr. 19, 2012. [Online]. Available: https://doi.org/10.1021/cr200301u
T. A. Mara and S. Tarantola, “Application of global sensitivity analysis of model output to building thermal simulations,” Building Simulation, vol. 1, Dic. 5, 2008. [Online]. Available: https://doi.org/10.1007/s12273-008-8129-5
K. J. Lomas and H. Eppel, “Sensitivity analysis techniques for building thermal simulation programs,” Energy and Buildings, vol. 19, no. 1, 1992. [Online]. Available: https://doi.org/10.1016/0378-7788(92)90033-D
V. Cheng and K. Steemers, “Modelling domestic energy consumption at district scale: A tool to support national and local energy policies,” Environmental Modelling & Software, vol. 26, no. 10, Oct. 2011.[Online]. Available: https://doi.org/10.1016/j.envsoft.2011.04.005
J. A. Díaz, S. Soutullo, E. Giancola, and J. A. Ferrer, “How the construction parameters influence the thermal loads of a building without internal gains,” in Proceedings of the III Ibero-American Conference on Smart Cities. Instituto Tecnológico de Costa Rica, 2020, pp. 166–180.
M. C. Peel, B. L. Finlayson, and T. A. McMahon, “Updated world map of the köppen-geiger climate classification,” Hydrology and Earth System Sciences, vol. 11, no. 5, Oct. 11, 2007. [Online]. Available: https://doi.org/10.5194/hess-11-1633-2007
M. de Fomento. (2020, Ene.) Documento básico HE: Ahorro de energía (CTE). Gobierno de España. [Online]. Available: https://www.codigotecnico.org/DocumentosCTE/AhorroEnergia.html
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