IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v237y2021ics0360544221017576.html
   My bibliography  Save this article

Optimizing energy efficiency and thermal comfort in building green retrofit

Author

Listed:
  • Li, Qing
  • Zhang, Lianying
  • Zhang, Limao
  • Wu, Xianguo

Abstract

Building green retrofit offers significant opportunities for enhancing energy efficiency and achieving green development goals. However, a conflicting criterion exists between energy conservation and thermal comfort improvement when making optimal design solutions for building retrofit. This study presents a simulation-based energy-comfort optimization model to facilitate evaluating various design alternatives and balancing multiple objectives in building green retrofit. A building simulation model is first established to measure energy consumption and comfort level. Then, a multi-objective optimization method (response surface method) is employed to identify critical building parameters and generates a set of alternative plans for building retrofit based on green building standards. After that, optimal design solutions with trade-offs between thermal comfort and energy demand are obtained. A school building in Wuhan city of China is chosen as a case to validate the developed model, and ten building parameters pertaining to energy demand and environmental comfort are considered in the optimization process. The results show that four parameters are significantly sensitive to energy efficiency and thermal comfort, including insulation thickness of the external wall, the heat transmission coefficient of the roof, solar heat gain coefficient of the external window, and window to wall ratio. The optimal combination of four parameters approximately produces 4 % of energy savings, as well as an improving level of environmental comfort. The study benefits designers and construction managers to determine optimal solutions for building retrofit to achieve better energy efficiency and comfort in green building development.

Suggested Citation

  • Li, Qing & Zhang, Lianying & Zhang, Limao & Wu, Xianguo, 2021. "Optimizing energy efficiency and thermal comfort in building green retrofit," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017576
    DOI: 10.1016/j.energy.2021.121509
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221017576
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121509?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Sheng & Sun, Yongjun & Cheng, Yong & Huang, Pei & Oladokun, Majeed Olaide & Lin, Zhang, 2018. "Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 1020-1031.
    2. Chong, Daokun & Zhu, Neng & Luo, Wei & Zhang, Zhiyu, 2019. "Broadening human thermal comfort range based on short-term heat acclimation," Energy, Elsevier, vol. 176(C), pages 418-428.
    3. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    4. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    5. Seal, Sayani & Boulet, Benoit & Dehkordi, Vahid R., 2020. "Centralized model predictive control strategy for thermal comfort and residential energy management," Energy, Elsevier, vol. 212(C).
    6. Pao-Hung Lin & Chin-Chuan Chang & Yu-Hui Lin & Wei-Liang Lin, 2019. "Green BIM Assessment Applying for Energy Consumption and Comfort in the Traditional Public Market: A Case Study," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    7. Moazzen, Nazanin & Ashrafian, Touraj & Yilmaz, Zerrin & Karagüler, Mustafa Erkan, 2020. "A multi-criteria approach to affordable energy-efficient retrofit of primary school buildings," Applied Energy, Elsevier, vol. 268(C).
    8. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    9. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    10. Hall, Samantha, 2014. "Development and initial trial of a tool to enable improved energy & human performance in existing commercial buildings," Renewable Energy, Elsevier, vol. 67(C), pages 109-118.
    11. Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kotarela, Faidra & Kyritsis, Anastasios & Agathokleous, Rafaela & Papanikolaou, Nick, 2023. "On the exploitation of dynamic simulations for the design of buildings energy systems," Energy, Elsevier, vol. 271(C).
    2. Konstantinos Sofias & Zoe Kanetaki & Constantinos Stergiou & Sébastien Jacques, 2023. "Combining CAD Modeling and Simulation of Energy Performance Data for the Retrofit of Public Buildings," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    3. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    4. Giulia Lamberti & Giacomo Salvadori & Francesco Leccese & Fabio Fantozzi & Philomena M. Bluyssen, 2021. "Advancement on Thermal Comfort in Educational Buildings: Current Issues and Way Forward," Sustainability, MDPI, vol. 13(18), pages 1-29, September.
    5. Chadly, Assia & Azar, Elie & Maalouf, Maher & Mayyas, Ahmad, 2022. "Techno-economic analysis of energy storage systems using reversible fuel cells and rechargeable batteries in green buildings," Energy, Elsevier, vol. 247(C).
    6. Lee, Minjung & Ham, Jeonggyun & Lee, Jeong-Won & Cho, Honghyun, 2023. "Analysis of thermal comfort, energy consumption, and CO2 reduction of indoor space according to the type of local heating under winter rest conditions," Energy, Elsevier, vol. 268(C).
    7. Renata Rapisarda & Francesco Nocera & Vincenzo Costanzo & Gaetano Sciuto & Rosa Caponetto, 2022. "Hydroponic Green Roof Systems as an Alternative to Traditional Pond and Green Roofs: A Literature Review," Energies, MDPI, vol. 15(6), pages 1-27, March.
    8. Simone Forastiere & Cristina Piselli & Benedetta Pioppi & Carla Balocco & Fabio Sciurpi & Anna Laura Pisello, 2023. "Towards Achieving Zero Carbon Targets in Building Retrofits: A Multi-Parameter Building Information Modeling (BIM) Approach Applied to a Case Study of a Thermal Bath," Energies, MDPI, vol. 16(12), pages 1-23, June.
    9. Xiaomiao Liao & Wanjiang Wang & Yihuan Zhou, 2023. "Investigating the Energy-Saving Effectiveness of Envelope Retrofits and Photovoltaic Systems: A Case Study of a Hotel in Urumqi," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    10. Silvia Erba & Alessandra Barbieri, 2022. "Measured Indoor Environmental Data in a Retrofitted Multiapartment Building to Assess Energy Flexibility and Thermal Safety during Winter Power Outages," Data, MDPI, vol. 7(7), pages 1-14, July.
    11. Margherita Mastellone & Silvia Ruggiero & Dimitra Papadaki & Nikolaos Barmparesos & Anastasia Fotopoulou & Annarita Ferrante & Margarita Niki Assimakopoulos, 2022. "Energy, Environmental Impact and Indoor Environmental Quality of Add-Ons in Buildings," Sustainability, MDPI, vol. 14(13), pages 1-29, June.
    12. Domenico Curto & Vincenzo Franzitta & Andrea Guercio & Domenico Panno, 2021. "Energy Retrofit. A Case Study—Santi Romano Dormitory on the Palermo University," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    13. Yanfei Ji & Guangchen Li & Fanghan Su & Yixing Chen & Rongpeng Zhang, 2023. "Retrofit Analysis of City-Scale Residential Buildings in the Hot Summer and Cold Winter Climate Zone," Energies, MDPI, vol. 16(17), pages 1-19, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Mengxue & Lai, Joseph, 2020. "Review on carbon emissions of commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    2. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    3. Meinrenken, Christoph J. & Mehmani, Ali, 2019. "Concurrent optimization of thermal and electric storage in commercial buildings to reduce operating cost and demand peaks under time-of-use tariffs," Applied Energy, Elsevier, vol. 254(C).
    4. Guo, Jing & Lin, Penghui & Zhang, Limao & Pan, Yue & Xiao, Zhonghua, 2023. "Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption," Applied Energy, Elsevier, vol. 350(C).
    5. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    6. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
    7. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    8. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    9. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    10. Ebrahim Morady & Madjid Soltani & Farshad Moradi Kashkooli & Masoud Ziabasharhagh & Armughan Al-Haq & Jatin Nathwani, 2022. "Improving Energy Efficiency by Utilizing Wetted Cellulose Pads in Passive Cooling Systems," Energies, MDPI, vol. 15(1), pages 1-17, January.
    11. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    12. Zhang, Sheng & Lin, Zhang & Ai, Zhengtao & Huan, Chao & Cheng, Yong & Wang, Fenghao, 2019. "Multi-criteria performance optimization for operation of stratum ventilation under heating mode," Applied Energy, Elsevier, vol. 239(C), pages 969-980.
    13. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    14. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    15. Picallo-Perez, Ana & Catrini, Pietro & Piacentino, Antonio & Sala, José-Mª, 2019. "A novel thermoeconomic analysis under dynamic operating conditions for space heating and cooling systems," Energy, Elsevier, vol. 180(C), pages 819-837.
    16. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    17. Małgorzata Fedorczak-Cisak & Katarzyna Nowak & Marcin Furtak, 2019. "Analysis of the Effect of Using External Venetian Blinds on the Thermal Comfort of Users of Highly Glazed Office Rooms in a Transition Season of Temperate Climate—Case Study," Energies, MDPI, vol. 13(1), pages 1-18, December.
    18. Burillo, Daniel & Chester, Mikhail V. & Pincetl, Stephanie & Fournier, Eric, 2019. "Electricity infrastructure vulnerabilities due to long-term growth and extreme heat from climate change in Los Angeles County," Energy Policy, Elsevier, vol. 128(C), pages 943-953.
    19. Shady Attia, 2020. "Spatial and Behavioral Thermal Adaptation in Net Zero Energy Buildings: An Exploratory Investigation," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    20. Li, Biao & Han, Zongwei & Bai, Chenguang & Hu, Honghao, 2019. "The influence of soil thermal properties on the operation performance on ground source heat pump system," Renewable Energy, Elsevier, vol. 141(C), pages 903-913.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017576. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.