Advertisement

Energy Efficiency

, Volume 12, Issue 7, pp 1891–1920 | Cite as

Assessing evidence-based single-step and staged deep retrofit towards nearly zero-energy buildings (nZEB) using multi-objective optimisation

  • Sheikh Zuhaib
  • Jamie GogginsEmail author
Original Article
First Online:

Abstract

There is a dearth of data and evidence in the literature to assist the industry in determining the most appropriate strategies for large-scale deep retrofitting of non-domestic buildings to achieve healthy low-energy buildings. Support to decision-making and enabling deep retrofit of these buildings requires approaches such as long-term renovation strategies and building renovation passports. This paper compares the impact of single-step and staged retrofit approaches to improve the building energy performance of an existing building to a nearly zero-energy building (nZEB) level with improved comfort and optimal life-cycle costs. The novel developed methodological framework is applied to a university building built in 1975 (partially retrofit in 2005) that is expected to be completely retrofitted in 2020. A set of scenarios are analysed for the case study building using a combination of retrofit measures towards achieving the cost-optimal non-dominated solutions (Pareto front) based on multiple-objective optimisation for the decision-maker. The results highlight that a single-step retrofit can achieve a reduction of up to 60% in primary energy consumption and reduction of 38% in discomfort hours. The findings also indicate that nZEB performance with the primary energy consumption in the range of ~ 75–90 kWh m−2 year−1 (with plug loads) can be achieved cost-effectively through single-step deep retrofit for a university building. Results also highlighted the inability to achieve higher energy performance or improved comfort in two stages relative to completing a deep retrofit in a single stage. The results aim to contribute to the existing debate on the economic and environmental feasibility in realising long-term renovation strategies for existing non-domestic buildings, especially university buildings.

Keywords

Nearly zero-energy buildings Deep retrofit Cost-optimality Energy performance Staged retrofits 

Nomenclature

Acronyms

BEC

Building energy communities

CvRMSE

Coefficient of variation of the root mean square error

DCV

Demand control ventilation

EPBD

Energy Performance of Building Directive

EPC

Energy Performance Certificate

GA

Genetic algorithm

HVAC

Heating, ventilation and air-conditioning

IAQ

Indoor air quality

IEQ

Indoor environmental quality

LCC

Life-cycle cost

MOO

Multi-objective optimisation

MV

Mechanical ventilation

MVHR

Mechanical ventilation with heat recovery

NMBE

Normalised mean bias error

NSGA

Non-dominated sorting genetic algorithm

NV

Natural ventilation

nZEB

Nearly zero-energy building

OH&P

Overhead and profit

PV

Photovoltaic

SEAI

Sustainable Energy Authority of Ireland

VAT

Value added tax

Symbols

DH

Percentage of discomfort hours [%]

g

Solar transmittance [−]

IC

Investment cost [€]

LOR

Light output ratio [−]

MR

Maintenance and repair cost [€]

NPV

Net present value [€ m−2]

OE

Operational energy cost [€]

PEC

Primary energy consumption per unit of conditioned area [kWh m−2 year−1]

Re

Replacement cost [€]

SHGC

Solar heat gain coefficient [−]

U

Thermal transmittance [W m−2 K−1]

VT

Visual transmittance [−]

This is a preview of subscription content, log in to check access.

Notes

Funding information

This work is financially supported by Science Foundation Ireland (SFI) (Grant No. 13/CDA/2200).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Andrade-Cabrera, C., Turner, W. J. N., Burke, D., Neu, O., & Finn, D. P. (2016). Lumped parameter building model calibration using particle swarm optimization. In 3rd Asia conference of International Building Performance Simulation Association (ASIM 2016). Jeju island, Korea.Google Scholar
  2. Artola, I., Rademaekers, K., Williams, R., & Yearwood, J. (2016). Boosting building renovation: What potential and value for Europe? Brussels: European Union.  https://doi.org/10.2861/331360.CrossRefGoogle Scholar
  3. Asadi, E., da Silva, M. G., Antunes, C. H., & Dias, L. (2012). A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and Environment, 56, 370–378.  https://doi.org/10.1016/j.buildenv.2012.04.005.CrossRefGoogle Scholar
  4. 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.  https://doi.org/10.1016/j.apenergy.2016.04.078.CrossRefGoogle Scholar
  5. Ascione, F., Bianco, N., De Masi, R. F., Mauro, G. M., & Vanoli, G. P. (2017). Energy retrofit of educational buildings: Transient energy simulations, model calibration and multi-objective optimization towards nearly zero-energy performance. Energy and Buildings, 144, 303–319.  https://doi.org/10.1016/j.enbuild.2017.03.056.CrossRefGoogle Scholar
  6. ASHRAE. (2013). ANSI/ASHRAE 55:2013 thermal environmental conditions for human occupancy. Atlanta: Ashrae, American Society of Heating, Refrigerating and Air-Conditioning Engineers.  https://doi.org/10.1007/s11926-011-0203-9.CrossRefGoogle Scholar
  7. ASHRAE. (2014). ASHRAE guideline 14–2014: Measurement of energy and demand savings. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  8. ASHRAE. (2016). ASHRAE Standard 62.1-2016—Ventilation for acceptable indoor air quality. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  9. ASHRAE. (2018). Standard 209–2018—Energy simulation aided design for buildings except low-rise residential buildings (ANSI approved). Atlanta: American Society of Heating, Refrigerating and Air-conditioning Engineers.Google Scholar
  10. Attia, S., Beltrán, L., De Herde, A., & Hensen, J. (2009). Architect friendly: A comparison of ten different building performance simulation tools. In Eleventh International IBPSA Conference (pp. 1–8). Glasgow. papers2://publication/uuid/C5D874D9-B854-4E97-980B-A555604BE791.Google Scholar
  11. Bandyopadhyay, S., & Saha, S. (2013). Unsupervised classification: Similarity measures, classical and metaheuristic approaches, and applications. Springer.  https://doi.org/10.1007/978-3-642-32451-2.CrossRefGoogle Scholar
  12. Becchio, C., Ferrando, D. G., Fregonara, E., Milani, N., Quercia, C., & Serra, V. (2016). The cost-optimal methodology for the energy retrofit of an ex-industrial building located in northern Italy. Energy and Buildings, 127, 590–602.  https://doi.org/10.1016/j.enbuild.2016.05.093.CrossRefGoogle Scholar
  13. Bleyl, J. W., Bareit, M., Casas, M. A., Chatterjee, S., Coolen, J., Hulshoff, A., Lohse, R., Mitchell, S., Robertson, M., & Ürge-Vorsatz, D. (2018). Office building deep energy retrofit: Life cycle cost benefit analyses using cash flow analysis and multiple benefits on project level. Energy Efficiency, 12, 261–279.  https://doi.org/10.1007/s12053-018-9707-8.CrossRefGoogle Scholar
  14. Blücher, M. (2016). Implementing deep energy step-by-step retrofits-EuroPHit: Increasing the European potential. Darmstadt. http://europhit.eu/sites/europhit.eu/files/EuroPHit_brochure_final_PHI.pdf. Accessed 19 Aug 2018.
  15. BPIE. (2010). Cost optimality—Discussing methology and challenges within the recast EPBD. Brussels: Building Performance Institute Europe.Google Scholar
  16. BPIE. (2011). Europe’s buildings under the microscope: A country-by-country review of the energy performance of buildings. Brussels: Buildings Performance Institute Europe.Google Scholar
  17. BPIE. (2015). Nearly zero energy buildings definitions across Europe. Belgium: Buildings Performance Institute Europe.Google Scholar
  18. BPIE. (2017). Factsheet—97% of buildings in the EU need to be upgraded. Brussels: Buildings Performance Institute Europe http://bpie.eu/wp-content/uploads/2017/12/State-of-the-building-stock-briefing_Dic6.pdf. Accessed 16 May 2018.
  19. BPIE. (2018). The concept of the individual building renovation roadmap—An in-depth case study of four frontrunner projects. Brussels: Building Performance Institute of Europe.Google Scholar
  20. Carolina, M. A., Cripps, A., Bouchlaghem, D., & Buswell, R. (2013). Benchmarking small power energy consumption in UK office buildings: A review of data published in CIBSE guide F. Building Services Engineering Research and Technology, 34(1), 73–86.CrossRefGoogle Scholar
  21. CEN. (2007). EN-15251: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics (Vol. 44). Brussels: European Commitee for Standardization.  https://doi.org/10.1520/E2019-03R13.Copyright.CrossRefGoogle Scholar
  22. CEN. (2017). EN 15459–1: 2017—Energy performance of buildings—economic evaluation procedure for energy systems in buildings—part 1: Calculation procedures, module M1–14. Brussels: European Commitee for Standardization.  https://doi.org/10.1520/E0872-82R13.2.CrossRefGoogle Scholar
  23. Chantrelle, F. P., Lahmidi, H., Keilholz, W., El Mankibi, M., & Michel, P. (2011). Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy, 88(4), 1386–1394.  https://doi.org/10.1016/j.apenergy.2010.10.002.CrossRefGoogle Scholar
  24. Congedo, P. M., D’Agostino, D., Baglivo, C., Tornese, G., & Zacà, I. (2016). Efficient solutions and cost-optimal analysis for existing school buildings. Energies, 9(10), 1–24.  https://doi.org/10.3390/en9100851.CrossRefGoogle Scholar
  25. CSO. (2018). Consumer Price Index— CSO—Central Statistics Office. https://www.cso.ie/en/statistics/prices/consumerpriceindex/. Accessed 25 September 2018.
  26. D’Agostino, D., Zangheri, P., & Castellazzi, L. (2017). Towards nearly zero energy buildings in Europe: A focus on retrofit in non-residential buildings. Energies, 10(1), 117.  https://doi.org/10.3390/en10010117.CrossRefGoogle Scholar
  27. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.  https://doi.org/10.1109/4235.996017.CrossRefGoogle Scholar
  28. DHPLG. (2017). Part-L conservation of fuel and energy—buildings other than dwellings. Dublin: Department of Housing, Planning and Local Government. http://www.housing.gov.ie/sites/default/files/publications/files/tgd_l_2017_for_buildings_other_than_dwellings.pdf http://www.environ.ie/en/Publications/DevelopmentandHousing/BuildingStandards/FileDownLoad,20322,en.pdf. Accessed 21 Aug 2018.
  29. DOE. (2010). EnergyPlus Manual V6.0, October 2010, Technical Report. USA: Department of Energy.Google Scholar
  30. DOE. (2019). eQUEST—The QUick Energy Simulation Tool. http://www.doe2.com/equest/. Accessed 15 April 2019.
  31. DPER. (2018). Project discount & and inflation rates. https://www.per.gov.ie/en/project-discount-inflation-rates/. Accessed 25 September 2018.
  32. EC. (2002). Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings. Office Journal of the European Union, 235, 12–25.  https://doi.org/10.1016/j.jclepro.2010.02.014.CrossRefGoogle Scholar
  33. EC. (2018). Commission welcomes Council adoption of new Energy Performance in Buildings Directive | European Commission. https://ec.europa.eu/info/news/commission-welcomes-council-adoption-new-energy-performance-buildings-directive-2018-may-14_en. Accessed 16 May 2018.
  34. ESB. (2018). Micro generation scheme—Electric Ireland. https://www.electricireland.ie/residential/help/micro-generation/electric-ireland-micro-generation-pilot-scheme. Accessed 3 October 2018.
  35. EU. (2009). Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009. Official Journal of the European Union, 140(16), 16–62.  https://doi.org/10.3000/17252555.L_2009.140.eng.CrossRefGoogle Scholar
  36. EU. (2010). Directive 2010/31/EU of the European Parliament and the Council of 19 May 2010 on the energy performance of buildings (recast). Office Journal of the European Union, 13–35.Google Scholar
  37. EU. (2012). Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency. Official Journal of the European Union, 1–56. doi: https://doi.org/10.3000/19770677.L_2012.315.eng
  38. EU. (2016). Commission recommendation (EU) 2016/1318. Official Journal of the European Union, 1038, 46–56.Google Scholar
  39. EU. (2018). Directive (EU) 2018/ of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency. Official Journal of the European Union, (October 2012), 75–91.Google Scholar
  40. Evins, R., Pointer, P., & Burgess, S. (2012). Multi-objective optimisation of a modular building for different climate types. In First Building Simulation and Optimization Conference-IBPSA (pp. 417–424). Loughborough.Google Scholar
  41. GBPN. (2013). What is a deep renovation definition—Technical report. Paris: Global Buildings Performance Network.Google Scholar
  42. Guglielmetti, R., Macumber, D., & Long, N. (2011). OpenStudio: An open source integrated analysis platform. 12th Conference of International Building Performance Simulation Association, (December), 442–449. http://www.osti.gov/bridge. Accessed 18 Apr 2018.
  43. Hamdy, M., Nguyen, A. T., & Hensen, J. L. M. (2016). A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energy and Buildings, 121, 57–71.  https://doi.org/10.1016/j.enbuild.2016.03.035.CrossRefGoogle Scholar
  44. Heo, Y., Choudhary, R., & Augenbroe, G. A. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47, 550–560.  https://doi.org/10.1016/j.enbuild.2011.12.029.CrossRefGoogle Scholar
  45. IEA. (2013). Modernising building energy codes to secure our global energy future: Policy pathway. New York: International Energy Agency.Google Scholar
  46. IES, V. (2018). IESVE software—Integrated environmental solutions. https://www.iesve.com/software. Accessed 23 September 2018.
  47. Irulegi, O., Ruiz-Pardo, A., Serra, A., Salmerón, J. M., & Vega, R. (2017). Retrofit strategies towards net zero energy educational buildings: A case study at the University of the Basque Country. Energy and Buildings, 144(2017), 387–400.  https://doi.org/10.1016/j.enbuild.2017.03.030.CrossRefGoogle Scholar
  48. LBNL. (2007). Standby Power: Data-Summary Table. http://standby.lbl.gov/summary-table.html. Accessed 23 October 2017.
  49. Lee, S. H., Hong, T., Piette, M. A., & Taylor-Lange, S. C. (2015). Energy retrofit analysis toolkits for commercial buildings: A review. Energy, 89, 1087–1100.  https://doi.org/10.1016/j.energy.2015.06.112.CrossRefGoogle Scholar
  50. Ma, Z., Cooper, P., Daly, D., & Ledo, L. (2012). Existing building retrofits: Methodology and state-of-the-art. Energy and Buildings, 55, 889–902.  https://doi.org/10.1016/j.enbuild.2012.08.018.CrossRefGoogle Scholar
  51. Mauro, G. M., Hamdy, M., Vanoli, G. P., Bianco, N., & Hensen, J. L. M. (2015). A new methodology for investigating the cost-optimality of energy retrofitting a building category. Energy and Buildings, 107, 456–478.  https://doi.org/10.1016/j.enbuild.2015.08.044.CrossRefGoogle Scholar
  52. Monetti, V., Davin, E., Fabrizio, E., André, P., & Filippi, M. (2015). Calibration of building energy simulation models based on optimization: A case study. Energy Procedia, 78, 2971–2976.  https://doi.org/10.1016/j.egypro.2015.11.693.CrossRefGoogle Scholar
  53. Nguyen, A. T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043–1058.  https://doi.org/10.1016/j.apenergy.2013.08.061.CrossRefGoogle Scholar
  54. NUIG. (2017). NUIG weather station. http://www.nuigalway.ie/iruse/weather.html. Accessed 8 September 2017.
  55. Penna, P., Prada, A., Cappelletti, F., & Gasparella, A. (2015). Multi-objectives optimization of energy efficiency measures in existing buildings. Energy and Buildings, 95, 57–69.  https://doi.org/10.1016/j.enbuild.2014.11.003.CrossRefGoogle Scholar
  56. Sajn, N. (2016). Energy efficiency of buildings: A nearly zero-energy future? Brussels: European Parliamentary Research Service.Google Scholar
  57. Sauchelli, M., Masera, G., D’Antona, G., & Manzolini, G. (2014). ISIS Facchinetti: A nearly zero energy retrofit in Italy. Energy Procedia, 48, 1326–1335.  https://doi.org/10.1016/j.egypro.2014.02.150.CrossRefGoogle Scholar
  58. SCSI. (2017). Construction cost index forecast. Dublin. http://www.aiqs.com.au/Publications/QuickDownloads/ConstructionCostIndexForecastWA.pdf. Accessed 3 Oct 2018.
  59. SEAI. (2017a). Calculations for Part L 2017 TGD: Buildings other than dwellings. Dublin: Sustainable Energy Authority of Ireland https://www.housing.gov.ie/sites/default/files/public-consultation/files/report_to_calculations_for_part_l_2017.pdf. Accessed 17 Aug 2018.
  60. SEAI. (2017b). Irelands solar chain opportunity. Dublin: Sustainable Energy Authority of Ireland.Google Scholar
  61. SEAI. (2018a). Better energy communities programme 2018. Dublin: Sustainable Energy Authority of Ireland.Google Scholar
  62. SEAI. (2018b). Solar PV pilot scheme guide. Dublin: Sustainable Energy Authority of Ireland.Google Scholar
  63. Sousa, J. (2012). Energy simulation software for buildings: Review and comparison. In Information Technology for Energy Applications (pp. 6–7). Lisbon. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.416.7812&rep=rep1&type=pdf. Accessed 05 Jul 2018.
  64. TRNSYS. (2018). TRNSYS: Transient system simulation tool. http://www.trnsys.com/. Accessed 23 September 2018.
  65. Yang, T., Pan, Y., Mao, J., Wang, Y., & Huang, Z. (2016). An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study. Applied Energy, 179, 1220–1231.  https://doi.org/10.1016/j.apenergy.2016.07.084.CrossRefGoogle Scholar
  66. Zangheri, P., Armani, R., Pietrobon, M., & Pagliano, L. (2018). Identification of cost-optimal and NZEB refurbishment levels for representative climates and building typologies across Europe. Energy Efficiency, 11(2), 337–369.  https://doi.org/10.1007/s12053-017-9566-8.CrossRefGoogle Scholar
  67. Zhang, Y. (2012). Use jeplus as an efficient building design optimisation tool. In CIBSE ASHRAE Technical Symposium (pp. 1–12). London.Google Scholar
  68. Zuhaib, S., Hajdukiewicz, M., Keane, M., & Goggins, J. (2015). Generic assessment of optimisation methods for performance based design of retrofitted building façades for nearly zero-energy buildings. In 10th Conference on Advanced Building Skins (pp. 425–433).  https://doi.org/10.13140/RG.2.1.4503.8962.
  69. Zuhaib, S., Manton, R., Hajdukiewicz, M., Keane, M., & Goggins, J. (2017). Attitudes and approaches of Irish retrofit industry professionals towards achieving nearly zero-energy buildings. International Journal of Building Pathology and Adaptation, 35(1), 16–40.  https://doi.org/10.1108/IJBPA-07-2016-0015.CrossRefGoogle Scholar
  70. Zuhaib, S., Manton, R., Griffin, C., Hajdukiewicz, M., Keane, M., & Goggins, J. (2018). An Indoor Environmental Quality (IEQ) assessment of a partiallyretrofitted university building. Building and Environment, 139, 69–85.  https://doi.org/10.1016/j.buildenv.2018.05.001.CrossRefGoogle Scholar
  71. Zuhaib, S., Hajdukiewicz, M., & Goggins, J. (2019). Application of a staged automated calibration methodology to a partially-retrofitted university building energy model. Journal of Building Engineering.  https://doi.org/10.1016/j.jobe.2019.100866.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Civil Engineering, School of Engineering, College of Science and EngineeringNational University of IrelandGalwayIreland
  2. 2.MaREI Centre for Marine, Climate and Energy, Ryan InstituteNational University of IrelandGalwayIreland

Personalised recommendations

Cite article

Buy options