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The Most Cost-Effective Energy Solution in Renovating a Multi-family House

  • Elaheh JalilzadehazhariEmail author
  • Krushna Mahapatra
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)

Abstract

The Swedish government aims to reduce total energy demand per heated building area by 50% until 2050. A large number of residential buildings, built within the so-called “Million homes program” in Sweden, need major renovations, which offers an opportunity to implement energy efficiency measures and thereby, reduce total energy demand. The best way to encourage the implementation of a major renovation is to demonstrate a practical method which reduces energy demand and provides economic benefits. Hence, this study aims to determine the most cost-effective energy solution in renovating a multi-family residential building. Multiple energy renovation measures were simulated on a case study to reduce the space heating and domestic hot water by 50%. The case study building was built within the “Million homes program” and is located in Växjö, Swedish climate zone 3. Design Builder software was used for analysing the pre-renovation energy performance of the building. The renovation measures comprised different insulation thicknesses of external walls, attic and ground floors, windows with different U-values, a mechanical ventilation with heat recovery system, and solar system for supporting space heating and domestic hot water. Later, a multi-objective optimization was accomplished for analysing every possible combination of renovation measures. The most cost-effective energy solution was obtained by calculating the net present value in a lifetime of 30 and 50 years and discount rate of 1, 3 and 5%. Comparing the implications of two different lifetimes on net present value with implications of three different discount rates on net present value shows that lifetime has more influence on net present value. Furthermore, the results show the capability of the multi-objective optimization method in analysing multiple renovation solution.

Keywords

Net present value Energy renovation measures Multi-objective optimization 

References

  1. 1.
    E. Recast, Directive 2010/31/EU of the European parliament and of the council of 19 May 2010 on the energy performance of buildings (recast). Official J. Eur. Union 18(06), 2010 (2010)Google Scholar
  2. 2.
    T. Niemelä, R. Kosonen, J. Jokisalo, Cost-optimal energy performance renovation measures of educational buildings in cold climate. Appl. Energy 183, 1005–1020 (2016)CrossRefGoogle Scholar
  3. 3.
    National audit Office, EU and Sweden’s climate and energy Climate targets, (In Swedish: EU:s och Sveriges klimat-och energimål) RiR 2013:19 Sweden 9 (2013)Google Scholar
  4. 4.
    Swedish Environmental Protection Agency, The environment control (Title is Swedish: Styr med sikte på miljömålen) (2015), pp. 1–140Google Scholar
  5. 5.
    L. Itard, F. Meijer, E. Vrins, H. Hoiting, Building renovation and modernisation in Europe: state of the art review. Final Report ERABUILD, Delft OTB Research Institute for Housing, Urban and Mobility Studies, Delft University of Technology 1–232 (2008)Google Scholar
  6. 6.
    Swedish energy agency, Energy Situation (Title in Swedish: Energiläget) (Bromma, Sweden) (2017), pp. 1–86Google Scholar
  7. 7.
    K. Mahapatra, S. Olsson, Energy performance of two multi-story wood-frame passive houses in Sweden. Buildings 5(4), 1207–1220 (2015)CrossRefGoogle Scholar
  8. 8.
    A. Dodoo, L. Gustavsson, R. Sathre, Life cycle primary energy implication of retrofitting a wood-framed apartment building to passive house standard. Resour. Conserv. Recycl. 54(12), 1152–1160 (2010)CrossRefGoogle Scholar
  9. 9.
    U. Janson, Passive houses in Sweden-experiences from design and construction, energy and building design (Lund University, Lund, 2008)Google Scholar
  10. 10.
    E. Kjellsson, Solar collectors combined with ground-source heat pumps in dwellings-analyses of system performance (Byggnadsfysik LTH, Lunds Tekniska Högskola, 2009)Google Scholar
  11. 11.
    F. Bonakdar, A. Sasic Kalagasidis, K. Mahapatra, The implications of climate zones on the cost-optimal level and cost-effectiveness of building envelope energy renovation and space heat demand reduction. Buildings 7(2), 39 (2017)CrossRefGoogle Scholar
  12. 12.
    F. Bonakdar, L. Gustavsson, A. Dodoo, Implications of Energy Efficiency Renovation Measures for a Swedish Residential Building on Cost, Primary Energy Use and Carbon Dioxide Emission, (ECEEE 2013, Belambra Les Criques, France, June 3–8, 2013), pp. 1287–1296Google Scholar
  13. 13.
    M. Gustafsson, M.S. Gustafsson, J.A. Myhren, C. Bales, S. Holmberg, Techno-economic analysis of energy renovation measures for a district heated multi-family house. Appl. Energy 177, 108–116 (2016)CrossRefGoogle Scholar
  14. 14.
    M. Gustafsson, C. Dipasquale, S. Poppi, A. Bellini, R. Fedrizzi, C. Bales, F. Ochs, M. Sié, S. Holmberg, Economic and environmental analysis of energy renovation packages for European office buildings. Energy Build. 148, 155–165 (2017)CrossRefGoogle Scholar
  15. 15.
    F.P. Chantrelle, H. Lahmidi, W. Keilholz, M.E. Mankibi, P. Michel, Development of a multicriteria tool for optimizing the renovation of buildings. Appl. Energy 88(4), 1386–1394 (2011)CrossRefGoogle Scholar
  16. 16.
    U. Janson, Passive Houses in Sweden. From Design to Evaluation of Four Demonstration Projects (Lund, Sweden) (2010), p. 390Google Scholar
  17. 17.
    Design Builder, 2017. www.designbuilder.co.uk
  18. 18.
    A.-T. Nguyen, S. Reiter, P. Rigo, A review on simulation-based optimization methods applied to building performance analysis. Appl. Energy 113, 1043–1058 (2014)CrossRefGoogle Scholar
  19. 19.
    A. Standard, Standard 55-2010: Thermal Environmental Conditions for Human Occupancy (ASHRAE, Atlanta, USA, 2010)Google Scholar
  20. 20.
    ISO7730-Standard, 7730, Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria, International Organization for Standardization: Geneva, Switzerland (2005)Google Scholar
  21. 21.
    National Board of Housing Building and Planning, Building Regulation (In Swedish: Boverkets byggregler, BFS 2011:6 ändrad t.o.m. BFS 2015:3Avsnitt 9 Energihushållning), National board of housing, building and planning [In Swedish: Boverket] (2015)Google Scholar
  22. 22.
    F. Tittarelli, F. Stazi, G. Politi, C. di Perna, P. Munafò, Degradation of Glass Mineral Wool Insulation after 25 Years in Masonry Cavity Walls (2013)Google Scholar
  23. 23.
    U.Y.A. Tettey, A. Dodoo, L. Gustavsson, Effects of different insulation materials on primary energy and CO2 emission of a multi-storey residential building. Energy Build. 82, 369–377 (2014)CrossRefGoogle Scholar
  24. 24.
    A. Kaufmann, H.M. Künzel, J. Radoń, Preventing moisture problems in retrofitted pitched roofs. ACTA Scientiarum Polonorum, Architectura 5(1), 69–79 (2006)Google Scholar
  25. 25.
    Swedish Energy Agency, FTX System for a House with 130 m2 of Living Space (In Swedish: FTX-aggregat hus med 130 m2 boyta) (2017). http://www.energimyndigheten.se/tester/tester-a-o/ftx-aggregat/ftx-aggregat-hus-med-130-m-boyta/. Last accessed 07 July 2017
  26. 26.
    S. Koziel, X.-S. Yang, Computational Optimization, Methods and Algorithms (Springer, Berlin, 2011)CrossRefGoogle Scholar
  27. 27.
    P. Hoes, J. Hensen, M. Loomans, B. De Vries, D. Bourgeois, User behavior in whole building simulation. Energy Build. 41(3), 295–302 (2009)CrossRefGoogle Scholar
  28. 28.
    Elitfonster, 2016. www.elitfonster.se
  29. 29.
    L. Gustavsson, A. Joelsson, Energy conservation and conversion of electrical heating systems in detached houses. Energy Build. 39(6), 717–726 (2007)CrossRefGoogle Scholar
  30. 30.
    S. Ruud, Economic Heating Systems for Low Energy Buildings–Calculation, Comparison and Evaluation of Different System Solutions (Borås, Sweden, SP Technical Research Institute of Sweden, 2010)Google Scholar
  31. 31.
    Å. Wahlström, Å. Blomsterberg, D. Olsson, Heat Recovery System for Existing Multi-Family Houses (In Swedish: Värmeåtervinningssystem för befintliga flerbostadshus). Pre study for technology procurement [In Swedish: Förstudie inför teknikupphandling] (2009)Google Scholar
  32. 32.
    NIBE, 2017. www.nibe.se. Last accessed 27 June 2017
  33. 33.
    Solra, 2017. www.solra.se
  34. 34.
    K. Blok, The Effectiveness of Policy Instruments for Energy-Efficiency Improvement in Firms: The Dutch Experience (Springer, Berlin, 2004)CrossRefGoogle Scholar
  35. 35.
    Byggshop, 2017. www.byggshop.se
  36. 36.
    M. Wetter, J. Wright, A comparison of deterministic and probabilistic optimization algorithms for non smooth simulation-based optimization. Build. Environ. 39(8), 989–999 (2004)CrossRefGoogle Scholar
  37. 37.
    L.R. Bernardo, H. Davidsson, E. Andersson, Retrofitted solar domestic hot water systems for swedish single-family houses—evaluation of a prototype and life-cycle cost analysis. Energies 9(11), 953 (2016)CrossRefGoogle Scholar
  38. 38.
    isolerproffs, 2017. http://www.isolerproffs.se. Last accessed 27 June 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Linnaeus UniversityVäxjöSweden

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