Skip to main content
SpringerLink
Log in

Optimal retrofitting scenarios of multi-objective energy-efficient historic building under different national goals integrating energy simulation, reduced order modelling and NSGA-II algorithm

  • Research Article
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Retrofitting a historic building under different national goals involves multiple objectives, constraints, and numerous potential measures and packages, therefore it is time-consuming and challenging during the early design stage. This study introduces a systematic retrofitting approach that incorporates standard measures for the building envelope (walls, windows, roof), as well as the heating, cooling, and lighting systems. Three retrofit objectives are delineated based on prevailing Chinese standards. The retrofit measures function as genes to optimize energy-savings, carbon emissions, and net present value (NPV) by employing a log-additive decomposition approach through energy simulation techniques and NSGA-II, yielding 185, 163, and 8 solutions. Subsequently, a weighted sum method is proposed to derive optimal solutions across multiple scenarios. The framework is applied to a courtyard building in Nanjing, China, and the outcomes of the implementation are scrutinized to ascertain the optimal retrofit package under various scenarios. Through this retrofit, energy consumption can be diminished by up to 63.62%, resulting in an NPV growth of 151.84%, and maximum rate of 60.48% carbon reduction. These three result values not only indicate that the optimal values are achieved in these three aspects of energy saving, carbon reduction and economy, but also show the possibility of possible equilibrium in this multi-objective optimization problem. The framework proposed in this study effectively addresses the multi-objective optimization challenge in building renovation by employing a reliable optimization algorithm with a computationally efficient reduced-order model. It provides valuable insights and recommendations for optimizing energy retrofit strategies and meeting various performance objectives.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdelrazek H, Yılmaz Y (2020). Methodology toward cost-optimal and energy-efficient retrofitting of historic buildings. Journal of Architectural Engineering, 26: 05020009.

    Article  Google Scholar 

  • Ali U, Shamsi MH, Bohacek M, et al. (2020). A data-driven approach to optimize urban scale energy retrofit decisions for residential buildings. Applied Energy, 267: 114861.

    Article  Google Scholar 

  • Alsaadani S, Bleil De Souza C (2019). Performer, consumer or expert? A critical review of building performance simulation training paradigms for building design decision-making. Journal of Building Performance Simulation, 12: 289–307.

    Article  Google Scholar 

  • Arasteh R, Ali Abbaspour R, Salmanmahiny A (2019). A modeling approach to path dependent and non-path dependent urban allocation in a rapidly growing region. Sustainable Cities and Society, 44: 378–394.

    Article  Google Scholar 

  • Baron RM, Kenny DA (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51: 1173–1182.

    Article  Google Scholar 

  • Battini F, Pernigotto G, Gasparella A (2023). District-level validation of a shoeboxing simplification algorithm to speed-up Urban Building Energy Modeling simulations. Applied Energy, 349: 121570.

    Article  Google Scholar 

  • Bertolin C, Loli A (2018). Sustainable interventions in historic buildings: A developing decision making tool. Journal of Cultural Heritage, 34: 291–302.

    Article  Google Scholar 

  • Besen P, Boarin P (2023). Integrating energy retrofit with seismic upgrades to future-proof built heritage: Case studies of unreinforced masonry buildings in Aotearoa New Zealand. Building and Environment, 241: 110512.

    Article  Google Scholar 

  • Buda A, Gori V, de Place Hansen EJ, et al. (2022). Existing tools enabling the implementation of EN 16883: 2017 Standard to integrate conservation-compatible retrofit solutions in historic buildings. Journal of Cultural Heritage, 57: 34–52.

    Article  Google Scholar 

  • Burke S (2017). Approaches to the conservation of twentieth-century cultural heritage. COMOS ISC20C.

  • Cabeza L, de Gracia Á, Pisello A (2018). Integration of renewable technologies in historical and heritage buildings: A review. Energy and Buildings, 177: 96–111.

    Article  Google Scholar 

  • Che W, Tso CY, Sun L, et al. (2019). Energy consumption, indoor thermal comfort and air quality in a commercial office with retrofitted heat, ventilation and air conditioning (HVAC) system. Energy and Buildings, 201: 202–215.

    Article  Google Scholar 

  • Chen S, Yang Y, Olomi C, et al. (2020a). Numerical study on the winter thermal performance and energy saving potential of thermo-activated PCM composite wall in existing buildings. Building Simulation, 13: 237–256.

    Article  Google Scholar 

  • Chen X, Qu K, Calautit J, et al. (2020b). Multi-criteria assessment approach for a residential building retrofit in Norway. Energy and Buildings, 215: 109668.

    Article  Google Scholar 

  • Chen R, Feng X, Li C, et al. (2021). Reduction in carbon dioxide emission and energy saving obtained by renovation of building envelope of existing residential buildings. Aerosol and Air Quality Research, 21: 210084.

    Article  Google Scholar 

  • Chidiac SE, Catania EJC, Morofsky E, et al. (2011). Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings. Energy, 36: 5037–5052.

    Article  Google Scholar 

  • Cho HM, Park JH, Wi S, et al. (2019). Energy retrofit analysis of cross-laminated timber residential buildings in Seoul, Korea: Insights from a case study of packages. Energy and Buildings, 202: 109329.

    Article  Google Scholar 

  • Cho HM, Yun BY, Yang S, et al. (2020). Optimal energy retrofit plan for conservation and sustainable use of historic campus building: case of cultural property building. Applied Energy, 275: 115313.

    Article  Google Scholar 

  • Coelho GBA, Silva HE, Henriques FMA (2018). Calibrated hygrothermal simulation models for historical buildings. Building and Environment, 142: 439–450.

    Article  Google Scholar 

  • Coelho GBA, Entradas Silva H, Henriques FMA (2020). Impact of climate change in cultural heritage: from energy consumption to artefacts’ conservation and building rehabilitation. Energy and Buildings, 224: 110250.

    Article  Google Scholar 

  • Coelho GBA, Henriques FMA (2021). Performance of passive retrofit measures for historic buildings that house artefacts viable for future conditions. Sustainable Cities and Society, 71: 102982.

    Article  Google Scholar 

  • De Jong K (2007). Parameter setting in EAs: A 30 year perspective. Studies in Computational Intelligence, 54: 1–18.

    Google Scholar 

  • Dehwah AHA, Haredy A, Krarti M (2022). Retrofit analysis of historical buildings to net-zero energy: Case study of the Ain Village, Saudi Arabia. Energy and Buildings, 258: 111826.

    Article  Google Scholar 

  • Fan Y, Xia X (2018). Energy-efficiency building retrofit planning for green building compliance. Building and Environment, 136: 312–321.

    Article  Google Scholar 

  • Ferrari S, Zanotto V (2016). Building Energy Performance Assessment in Southern Europe. In: SpringerBriefs in Applied Sciences and Technology. Cham, Switzerland: Springer.

    Google Scholar 

  • Giannakis GI, Kontes GD, Korolija I, et al. (2017). Simulation-time reduction techniques for a retrofit planning tool. In: Proceedings of the 15th International IBPSA Building Simulation Conference.

  • González-Caballín Sánchez JM, Meana-Fernández A, Ríos-Fernández JC, et al. (2023). Characterization of housing stock for energy retrofitting purposes in Spain. Building Simulation, 16: 947–962.

    Article  Google Scholar 

  • Grosvenor A, Zemlyansky A, Deighan D, et al. (2022). Simulation workflows in minutes, at scale for next-generation HPC. In: Doug K, Al G, Pophale S, Liu H, Parete-Koon S (eds), Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation. Cham, Switzerland: Springer Nature Switzerland

    Google Scholar 

  • Hart R, Selkowitz S, Curcija C (2019). Thermal performance and potential annual energy impact of retrofit thin-glass triple-pane glazing in US residential buildings. Building Simulation, 12: 79–86.

    Article  Google Scholar 

  • Hendricken L, Wen J, Gurian PL (2019). Development of a new reduced order model for predicting the energy savings of multi-ECM permutations. Energy and Buildings, 182: 287–299.

    Article  Google Scholar 

  • Hong T, Buhl F, Haves P (2008). EnergyPlus Run Time Analysis. Medium: ED; Size: 75. Available at https://www.osti.gov/servlets/purl/951766.

  • Hong X, Shi F, Wang S, et al. (2021). Multi-objective optimization of thermochromic glazing based on daylight and energy performance evaluation. Building Simulation, 14: 1685–1695.

    Article  Google Scholar 

  • Ikuzwe A, Ye X, Xia X (2020). Energy-maintenance optimization for retrofitted lighting system incorporating luminous flux degradation to enhance visual comfort. Applied Energy, 261: 114379.

    Article  Google Scholar 

  • Jiang Z, Risbeck MJ, Ramamurti V, et al. (2021). Deep reinforcement learning for energy cost optimization in building HVAC systems. ASHRAE Transactions, 127(1): 38–47.

    Google Scholar 

  • Kadrić D, Aganovic A, Martinović S, et al. (2022). Cost-related analysis of implementing energy-efficient retrofit measures in the residential building sector of a middle-income country—A case study of Bosnia and Herzegovina. Energy and Buildings, 257: 111765.

    Article  Google Scholar 

  • Kadrić D, Aganović A, Kadrić E (2023). Multi-objective optimization of energy-efficient retrofitting strategies for single-family residential homes: Minimizing energy consumption, CO2 emissions and retrofit costs. Energy Reports, 10: 1968–1981.

    Article  Google Scholar 

  • Kalamees T (2004). IDA ICE: The simulation tool for making the whole building energy and HAM analysis. IEA Annex 41 MOIST-ENG.

  • Li M, Yao X (2019). Quality evaluation of solution sets in multiobjective optimisation: A survey. ACM Computing Surveys, 52: 26.

    Google Scholar 

  • Lin Z, Hong T, Xu X, et al. (2023). Evaluating energy retrofits of historic buildings in a university campus using an urban building energy model that considers uncertainties. Sustainable Cities and Society, 95: 104602.

    Article  Google Scholar 

  • Ma D, Li X, Lin B, et al. (2023). An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives. Energy, 263: 125704.

    Article  Google Scholar 

  • Mauri L, Vallati A, Ocłoń P (2019). Low impact energy saving strategies for individual heating systems in a modern residential building: A case study in Rome. Journal of Cleaner Production, 214: 791–802.

    Article  Google Scholar 

  • Mazzeo D, Matera N, Cornaro C, et al. (2020). EnergyPlus, IDA ICE and TRNSYS predictive simulation accuracy for building thermal behaviour evaluation by using an experimental campaign in solar test boxes with and without a PCM module. Energy and Buildings, 212: 109812.

    Article  Google Scholar 

  • Misiopecki C, Grynning S, Gustavsen A (2023). Thermal improvements of box-window using shading attachments. Hot-box measurements. Developments in the Built Environment, 16: 100185.

    Article  Google Scholar 

  • MOHURD (2008). JGJT 151-2008: Calculation Specification for Thermal Performance of Windows, Doors And Glass Curtain-Walls. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • MOHURD (2015). GB/T51141-2015: Assessment Standard for Green Retrofitting of Existing Building. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • MOHURD (2018a). GB/T50357-2018: Standard of Conservation Planning for Historie City. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • MOHURD (2018b). GB50016-2018: Code for fire protection design of buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • MOHURD (2019). GB/T 51366-2019: Standard for Building Carbon Emission Calculation. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • MOHURD (2021). GB 55015-2021: General Code for Energy Efficiency and Renewable Energy Application in Buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)

  • Monetary Policy Division (n.d.). National Interbank Offered Rate (NIBOR) Authorised to Publish Loan Market Quotation Rates (LPR) Notice.

  • Na R, Shen Z (2021). Assessing cooling energy reduction potentials by retrofitting traditional cavity walls into passively ventilated cavity walls. Building Simulation, 14: 1295–1309.

    Article  Google Scholar 

  • Nanjing Municipal Costing Station (2023). Nanjing (including Jiangning, Liuhe, Gaochun three districts) construction materials market information prices. Available at https://www.njszj.cn/ZJWEB/MessageShow.aspx?Id=d9da15ca-9b42-4393-89bb-1350ad469475

  • Passer A, Lützkendorf T, Habert G, et al. (2020). Sustainable built environment: transition towards a net zero carbon built environment. International Journal of Life Cycle Assessment, 25: 1160–1167.

    Article  Google Scholar 

  • Philips (2017). philips indoor-luminaires list. Available at https://www.lighting.philips.com.cn/prof/indoor-luminaires.

  • Piccinini A, Hajdukiewicz M, Keane MM (2021). A novel reduced order model technology framework to support the estimation of the energy savings in building retrofits. Energy and Buildings, 244: 110896.

    Article  Google Scholar 

  • Posani M, Do Rosario Veiga M, de Freitas VP (2021). Towards resilience and sustainability for historic buildings: A review of envelope retrofit possibilities and a discussion on hygric compatibility of thermal insulations. International Journal of Architectural Heritage, 15: 807–823.

    Article  Google Scholar 

  • Rhee J, Kim E (2020). Digital Media Series: Grasshopper. Available at https://www.researchgate.net/publication/368861172_Digital_Media_Series_Grasshopper

  • Ruggeri AG, Calzolari M, Scarpa M, et al. (2020). Planning energy retrofit on historic building stocks: A score-driven decision support system. Energy and Buildings, 224: 110066.

    Article  Google Scholar 

  • Simões GMF, Leder SM (2022). Energy poverty: The paradox between low income and increasing household energy consumption in Brazil. 268: 112234.

  • Song J, Wang W, Ni P, et al. (2023). Framework on low-carbon retrofit of rural residential buildings in arid areas of Northwest China: A case study of Turpan residential buildings. Building Simulation, 16: 279–297.

    Article  Google Scholar 

  • Standardization Administration of China (2019). GB 21455-2019: The minimum allowable value of the energy efficiency and energy efficiency grades for room air conditioners. Standardization Administration of China. (in Chinese)

  • Standing Committee of the Nanjing Municipal People’s Congress (2021). Regulations on the Protection of Nanjing Historical and Cultural Cities. Standing Committee of the Nanjing Municipal People’s Congress. (in Chinese)

  • State Council of China (2021). Opinions on Strengthening the Protection and Transmission of History and Culture in Urban and Rural Construction. State Council of China. Availabe at https://www.gov.cn/gongbao/content/2021/content_5637945.htm (in Chinese)

  • Surana A, Taylor RD, Narayanan S, et al. (2012). Rapid assessment of deep retrofit system solutions to improve energy efficiency in DoD installations and buildings. In: Proceedings of International High Performance Buildings Conference at Purdue.

  • Tavakolan M, Mostafazadeh F, Jalilzadeh Eirdmousa S, et al. (2022). A parallel computing simulation-based multi-objective optimization framework for economic analysis of building energy retrofit: A case study in Iran. Journal of Building Engineering, 45: 103485.

    Article  Google Scholar 

  • Timuçin H (2017). Energy efficient building design development: A retrospective approach. In: Proceedings of International Symposium on Energy Efficiency in Buildings.

  • Verma G (2022). Secure VM migration in cloud: Multi-criteria perspective with improved optimization model. Wireless Personal Communications, 124: 75–102.

    Article  Google Scholar 

  • Wang CN, Nhieu NL, Nguyen HP, et al. (2021). Simulation-based optimization integrated multiple criteria decision-making framework for wave energy site selection: A case study of Australia. IEEE Access, 9: 167458–167476.

    Article  Google Scholar 

  • Wang W, Liu K, Zhang M, et al. (2021). From simulation to data-driven approach:Aa framework of integrating urban morphology to low-energy urban design. Renewable Energy, 179: 2016–2035.

    Article  Google Scholar 

  • Wang M, Chen C, Fan B, et al. (2023). Multi-objective optimization of envelope design of rural tourism buildings in southeastern coastal areas of China based on NSGA-II algorithm and entropy-based TOPSIS method. Sustainability, 15: 7238.

    Article  Google Scholar 

  • Yan D, Zhou X, An J, et al. (2022). DeST 3.0: A new-generation building performance simulation platform. Building Simulation, 15: 1849–1868.

    Article  Google Scholar 

  • Zhong F, Calautit JK, Wu Y (2022). Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change. Energy, 258: 124762.

    Article  Google Scholar 

  • Zhuhai Gree Electric Co. (2023). Gree hanging air conditioner list. Available at https://gree.com/cmsProduct/list/

Download references

Acknowledgements

The work described in this paper was sponsored by the National Key R&D Program-Strategic Scientific and Technological Innovation Cooperation (#2022YFE0208600), National Science and Foundation of China (#52208011), the Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX23_0034). Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of those organizations.

Author information

Authors and Affiliations

  1. School of Architecture, Southeast University, Nanjing, Jiangsu Province, 210096, China

    Hailu Wei, Yuanhao Jiao, Wei Wang & Tong Zhang

  2. Key Laboratory of Urban and Architectural Heritage Conservation (Southeast University), Ministry of Education, Nanjing, China

    Wei Wang

  3. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Zhe Wang

  4. HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China

    Zhe Wang

Authors
  1. Hailu Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanhao Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhe Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wei Wang or Tong Zhang.

Ethics declarations

The authors have no competing interests to declare that are relevant to the content of this article. Zhe Wang is a Subject Editor of Building Simulation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, H., Jiao, Y., Wang, Z. et al. Optimal retrofitting scenarios of multi-objective energy-efficient historic building under different national goals integrating energy simulation, reduced order modelling and NSGA-II algorithm. Build. Simul. (2024). https://doi.org/10.1007/s12273-024-1122-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12273-024-1122-9

Keywords

Search

Navigation