Advertisement

A tool for the assessment of energy-efficiency retrofit packages based on simulations, for single-family housing in Concepcion, Chile

  • Rodrigo García-Alvarado
  • Pedro G. Campos
Original Article
  • 45 Downloads

Abstract

A variety of retrofit options can be undertaken to reduce the growing energy consumption and ensure indoor comfort levels in housing. These offer specific solutions whose energy performance can be determined using building thermal performance simulations. This article presents a computer tool to help analyse alternatives to improve energy performance for single-family housing in the city of Concepción, Chile. This software provides a novel procedure to manage thermal simulations, defining retrofit packages based on simulation results and that include price calculations. The software also allows estimates to be made using data from previously studied housing in the region. Background data are presented on the housing type under study as well as the proposed retrofit options and the analysis of some case studies. The software can suggest retrofit packages which provide reductions that range from a quarter to half the total energy demand at a cost of between 5 and 15% of total house value. These results show that the proposed tool works properly and provides good energy-efficiency retrofit recommendations for this type of housing, allowing it to be used as an innovative public dissemination instrument. Furthermore, it can be extended to manage simulations of other dwelling typologies or regions to support professional work.

Keywords

Building retrofit Energy-efficiency of housing Single-family dwellings Chile 

Notes

Funding

This study was funded by FONDECYT 1120165.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Albatici, R., Gadotti, A., Baldessari, C., & Chiogna, M. (2016). A decision making tool for a comprehensive evaluation of building retrofitting actions at the regional scale. Sustainability, 8(10), 990.  https://doi.org/10.3390/su8100990.CrossRefGoogle Scholar
  2. American Society of Heating Refrigerating and Air-Conditioning Engineers (2016) ASRHAE 2016 90.1 ANSI/ASHRAE/IES Standard 90.1-2016—Energy Standard for Buildings Except Low-Rise Residential Buildings. https://www.ashrae.org/resources--publications/bookstore/standard-90-1
  3. Anderson, K. (2014). Design energy simulation for architects: Guide to 3D graphics. New York: Routledge.Google Scholar
  4. Anderson, R. & Christensen, C. (2006). Program design analysis using BEopt building energy optimization software: Defining a technology pathway leading to new homes with zero peak cooling demand. In: Proc. 2006 ACEEE Summer Study Energy Efficiency in Buildings, Pacific Grove, California. https://www.nrel.gov/tech_deployment/pdfs/nrel_be_opt_tool.pdf
  5. Ashuri, B., Kashani, H. & Lu, J. (2011). An investment analysis framework for energy retrofit in existing buildings. In: Proc. 47th. ASC Annual International Conference. http://ascpro0.ascweb.org/archives/cd/2011/paper/CPRT364002011.pdf
  6. Attia, S., Beltrán, L., De Herde, A. & Hensen, J. (2009). “Architect friendly”: A comparison of ten different building performance simulation tools. In: Proc. 11th. International IBPSA Conference, Glasgow, Scotland. http://www.ibpsa.org/proceedings/bs2009/bs09_0204_211.pdf.
  7. Attia, S., Gratia, E., De Herde, A., & Hensen, J. (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings, 49, 2–15.  https://doi.org/10.1016/j.enbuild.2012.01.028.CrossRefGoogle Scholar
  8. Bennett, K.H., & Rajlich, V.T. (2000). Software maintenance and evolution: a roadmap. In: Proc. Conference on the Future of Software Engineering—ICSE ‘00, ACM Press, New York, USA: pp. 73–87.  https://doi.org/10.1145/336512.336534.
  9. Bustamante, W., Rozas, Y., Encinas, F., Martínez, P. & Cepeda R. (2009). Guía de diseño para la eficiencia energética en la vivienda social. Santiago: Ministerio de Vivienda y Urbanismo. https://dial.uclouvain.be/downloader/downloader.php?pid=boreal:91592&datastream=PDF_01. Accessed 11 Aug 2017.
  10. Carassus, J. (2013). The implementation of energy efficient buildings policies: An international comparison. Rotterdam: International Council for Research and Innovation in building and construction, task Group 66. https://heyblom.websites.xs4all.nl/website/newsletter/1308/tg66_publication.pdf. Accessed 11 Aug 2017.
  11. Corporación de Desarrollo Tecnológico (2010). Estudio de Usos Finales y Curva de Oferta de Conservación de la Energía en el Sector Residencial, Santiago: Cámara Chilena de la Construcción. http://dataset.cne.cl/Energia_Abierta/Estudios/Minerg/Usos%20finales%20y%20curva%20de%20oferta%20de%20conservaci%C3%B3n%20de%20la%20energ%C3%ADa%20en%20el%20sector%20de%20residencial%20de%20Chile.pdf. Accessed 11 Aug 2017.
  12. Csik, Á., & Csoknyai, T. (2014). Defining energy- and cost-saving potentials and their application in optimal building refurbishment. Environmental Engineering and Management Journal, 13(11), 2771–2779 http://www.eemj.icpm.tuiasi.ro/pdfs/vol13/no11/Full/9_679_Csik_14.pdf.Google Scholar
  13. Csoknyai, T., Hrabovszky-Horváth, S., Georgiev, Z., Jovanovic-Popovic, M., Stankovic, B., Villatoro, O., & Szendrő, G. (2016). Building stock characteristics and energy performance of residential buildings in eastern-European countries. Energy and Buildings, 132, 39–52.  https://doi.org/10.1016/j.enbuild.2016.06.062.CrossRefGoogle Scholar
  14. Dascalaki, E. G., Droutsa, K. G., Balaras, C. A., & Kontoyiannidis, S. (2011). Building typologies as a tool for assessing the energy performance of residentialbuildings—a case study for the Hellenic building stock. Energy and Buildings, 43(12), 3400–3409.  https://doi.org/10.1016/j.enbuild.2011.09.002.CrossRefGoogle Scholar
  15. Diario Oficial de Chile (2017). Anteproyecto del Plan de Prevención y Descontaminación Atmosférica para las Comunas de Concepción Metropolitano. Diario Oficial de la República de Chile 41.733, pp. 1–12. http://planesynormas.mma.gob.cl/archivos/2017/proyectos/Publicacion_Diario_Oficial_Anteproyecto.pdf. Accessed 17 Jan 2018.
  16. Dosal, C. (2013). Eficiencia energética y ambiental en el sector vivienda. Revisión de prácticas nacionales e internacionales. México: Fundación Idea y Embajada Británica en México. http://fundacionidea.org.mx/assets/files/FIdea_libro%20eficiencia%20energetica%20final.pdf. Accessed 11 Aug 2017.
  17. Echeverría, B. (2007). Manual de Aplicación Reglamentación Térmica. Santiago: Ministerio de Vivienda y Urbanismo, Chile http://www.minvu.cl/opensite_20070417155724.aspx.. Accessed 11 Aug 2017.Google Scholar
  18. Esan, K.O. (2012). Analysis of housing upgrades for policy formulation using dynamic simulation. Master thesis. Glasgow: University of Strathclyde. http://www.esru.strath.ac.uk/Documents/MSc_2012/Esan.pdf
  19. Fissore, A. (2009). La realidad energética en el sector residencial de la región del Bío-Bío. Santiago: Alianza de Energía y Clima de las Américas http://ecpamericas.org/data/files/Initiatives/energy_efficiency_working_group/eewg_chile_workshop_mission_2012/Presenta-AFS-ECPA.pdf. Accessed 11 Aug 2017.Google Scholar
  20. Fissore, A., & Colonelli, P. (2013). Evaluación Independiente del Programa de Reacondicionamiento Térmico. Santiago: Ministerio de Vivienda y Urbanismo y Ministerio de Energía, Chile http://dataset.cne.cl/Energia_Abierta/Estudios/Minerg/24_Evaluaci%C3%B3n%20Independiente%20del%20Prog%20de%20Reacondicionamiento%20T%C3%A9rmico_Soluciones%20Energ%C3%A9ticas_584105-18_LP11.pdf. Accessed 11 Augs 2017.Google Scholar
  21. Florentzou, F., Genre, J. L., & Roulet, C. A. (2002). TOBUS software—An interactive decision aid tool for building retrofit studies. Energy and Buildings, 34(2), 193–202.  https://doi.org/10.1016/S0378-7788(01)00108-6.CrossRefGoogle Scholar
  22. Galvin, R., & Sunikka-Blank, M. (2013). Economic viability in thermal retrofit policies: Learning from ten years of experience in Germany. Energy Policy, 54, 343–351.  https://doi.org/10.1016/j.enpol.2012.11.044.CrossRefGoogle Scholar
  23. Garcia Alvarado, R., Soto, J., Muñoz, C., Bobadilla, A., Herrera, R., & Bustamante, W. (2014). Analysis of energy-efficiency improvements in single-family dwellings in Concepcion, Chile. Open House International, 39(2), 57–68 http://www.academia.edu/download/40201536/OHI_Vol.39_No.2.pdf#page=58.Google Scholar
  24. Gosling, J., Joy, B. & Steele G. (1996). The Java Language Specification, 1996. https://docs.oracle.com/javase/specs/jls/se6/html/j.preface.html. Accessed 11 August 2017.
  25. Harvey, L. D. D. (2009). Reducing energy use in the buildings sector: Measures, costs, and examples. Energy Efficiency, 2(2), 139–163.  https://doi.org/10.1007/s12053-009-9041-2.CrossRefGoogle Scholar
  26. Hendron, R., & Engebrecht, C. (2010). Building America house simulation protocols. Department of Energy: U.S. Washington. https://www.nrel.gov/docs/fy11osti/49246.pdf. Accessed 11 Aug 2017.Google Scholar
  27. Hensen, J., & Lamberts, R. (Eds.). (2012). Building performance simulation for design and operation. New York: Routledge.Google Scholar
  28. International Organization for Standarization (2008) ISO 13790:2008, Energy performance of buildings—Calculation of energy use for space heating and cooling. https://www.iso.org/standard/41974.html
  29. Jacobson, I. (1992). Object oriented software engineering: A use case driven approach. New York: Addison-Wesley Professional.zbMATHGoogle Scholar
  30. Jankovic, L. (2012). Designing zero carbon buildings using dynamic simulation methods. London and New York: Routledge.Google Scholar
  31. Justiniano, C., Márquez, F. & D’Alençon R. (2008). Parámetros y estándares de habitabilidad: Calidad en la vivienda, el entorno inmediato y el conjunto habitacional. In: Camino Al Bicenten. Propuestas Para Chile, Santiago: P. Universidad Católica de Chile, pp. 271–304. http://politicaspublicas.uc.cl/wp-content/uploads/2015/02/parametros-y-estandares-de-habitabilidad.pdf
  32. Larman, C. (2005). Applying UML and patterns: An introduction to object-oriented analysis and design and iterative development, 3rd ed. Pearson Education, Inc.Google Scholar
  33. Loy, M., Eckstein, R., Wood, D., Elliott, J. & Cole B. (2003). Java swing, 2nd ed., O’Reilly Media, Inc.Google Scholar
  34. Ministerio de Vivienda y Urbanismo, (2014). Listado Oficial de Soluciones Constructivas para Acondicionamiento Térmico, Santiago: Ministerio de Vivienda y Urbanismo. http://www.minvu.cl/incjs/download.aspx?glb_cod_nodo=20070606164405&hdd_nom_archivo=Listado%20T%C3%A9rmico%2011.pdf. Accessed 11 Aug 2017.
  35. Morgan, R., Gilman, D., Mukhopadhyay, J., Marshall, K., Stackhouse, R., Cordes, J., et al. (2007). Development of a residential code-compliant calculator for the Texas climate vision project. In Proc. 15.5 Symposium on Improving Building Systems in Hot and Humid Climates, San Antonio, TX. https://pdfs.semanticscholar.org/1caa/135d409b42259e7a7e3a3b194962b18e7657.pdf
  36. Ondac (2017). Manual de Precios. https://www.ondac.com/602/w3-article-69662.html. Accessed 10 Aug 2017.
  37. Persson, M. L., Roos, A., & Wall, M. (2006). Influence of window size on the energy balance of low energy houses. Energy and Buildings, 38(3), 181–188.  https://doi.org/10.1016/j.enbuild.2005.05.006.CrossRefGoogle Scholar
  38. Pino-Mejias, R., Pérez-Fargallo, A., Rubio-Bellido, C., & Pulido-Arcas, J. A. (2017). Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions. Energy, 118, 24–36.  https://doi.org/10.1016/j.energy.2016.12.022.CrossRefGoogle Scholar
  39. Polly, B., Gestwick, M., Bianchi, M., Anderson, R., Horowitz, S., Christensen, C., et al. (2011). A method for determining optimal residential energy efficiency retrofit packages, U.S. Washington: Department of Energy. https://www.nrel.gov/docs/fy11osti/50572.pdf. Accessed 11 Aug 2017.
  40. Roberts, D. (2012). Pathways to cost-effective retrofit savings, Washington: Department of Energy. http://online.etm.pdx.edu/bpa_summit/presentation/092412_b_1a_Roberts.pdf. Accessed 11 Aug 2017.
  41. Rubio-Bellido, C., Pérez-Fargallo, A., Pulido-Arcas, J., & Trebilcock, M. (2017). Application of adaptive comfort behaviors in Chilean social housing standards under the influence of climate change. Building Simulation, 2017, 1–15.  https://doi.org/10.1007/s12273-017-0385-9.Google Scholar
  42. Stein, B., Loga, T. & Diefenbach, N., eds. (2015), Monitor progress towards climate targets in European housing stocks, main results of the EPISCOPE Project. http://EPISCOPE.eu/. Accessed 11 Aug 2017.
  43. Xu, P., Xu, T., & Shen, P. (2013). Energy and behavioral impacts of integrative retrofits for residential buildings: What is at stake for building energy policy reforms in northern China? Energy Policy, 52, 667–676.  https://doi.org/10.1016/j.enpol.2012.10.029.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Depto. Diseño y Teoría de la ArquitecturaUniversidad del Bío-BíoConcepciónChile
  2. 2.Depto. Sistemas de InformaciónUniversidad del Bío-BíoConcepciónChile

Personalised recommendations