IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v8y2016i10p1063-d81123.html
   My bibliography  Save this article

A Bottom-Up Building Stock Model for Tracking Regional Energy Targets—A Case Study of Kočevje

Author

Listed:
  • Marjana Šijanec Zavrl

    (Building and Civil Engineering Institute ZRMK, Dimičeva 12, 1000 Ljubljana, Slovenia
    European Faculty of Law, Cankarjevo nabrežje 11, 1000 Ljubljana, Slovenia)

  • Gašper Stegnar

    (Building and Civil Engineering Institute ZRMK, Dimičeva 12, 1000 Ljubljana, Slovenia)

  • Andraž Rakušček

    (Building and Civil Engineering Institute ZRMK, Dimičeva 12, 1000 Ljubljana, Slovenia)

  • Henrik Gjerkeš

    (Building and Civil Engineering Institute ZRMK, Dimičeva 12, 1000 Ljubljana, Slovenia
    Centre for Systems and Information Technologies, University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia)

Abstract

The paper addresses the development of a bottom-up building stock energy model (BuilS) for identification of the building stock renovation potential by considering energy performance of individual buildings through cross-linked data from various public available databases. The model enables integration of various EE and RES measures on the building stock to demonstrate long-term economic and environmental effects of different building stock refurbishment strategies. In the presented case study, the BuilS model was applied in the Kočevje city area and validated using the measured energy consumption of the buildings connected to the city district heating system. Three strategies for improving the building stock in Kočevje towards a more sustainable one are presented with their impact on energy use and CO 2 emission projections up to 2030. It is demonstrated that the BuilS bottom-up model enables the setting of a correct baseline regarding energy use of the existing building stock and that such a model is a powerful tool for design and validation of the building stock renovation strategies. It is also shown that the accuracy of the model depends on available information on local resources and local needs, therefore acceleration of the building stock monitoring on the level of each building and continually upgrading of databases with building renovation information is of the utmost importance.

Suggested Citation

  • Marjana Šijanec Zavrl & Gašper Stegnar & Andraž Rakušček & Henrik Gjerkeš, 2016. "A Bottom-Up Building Stock Model for Tracking Regional Energy Targets—A Case Study of Kočevje," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1063-:d:81123
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/8/10/1063/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/8/10/1063/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caputo, Paola & Costa, Gaia & Ferrari, Simone, 2013. "A supporting method for defining energy strategies in the building sector at urban scale," Energy Policy, Elsevier, vol. 55(C), pages 261-270.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    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. Mikael Mangold & Magnus Österbring & Conny Overland & Tim Johansson & Holger Wallbaum, 2018. "Building Ownership, Renovation Investments, and Energy Performance—A Study of Multi-Family Dwellings in Gothenburg," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    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. Gašper Stegnar, 2025. "Bridging the Gap to Decarbonization: Evaluating Energy Renovation Performance and Compliance," Energies, MDPI, vol. 18(5), pages 1-22, February.
    4. Joanne Louise Patterson, 2016. "Evaluation of a Regional Retrofit Programme to Upgrade Existing Housing Stock to Reduce Carbon Emissions, Fuel Poverty and Support the Local Supply Chain," Sustainability, MDPI, vol. 8(12), pages 1-21, December.

    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. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    2. Paola Caputo & Costa Gaia & Valentina Zanotto, 2013. "A Methodology for Defining Electricity Demand in Energy Simulations Referred to the Italian Context," Energies, MDPI, vol. 6(12), pages 1-19, December.
    3. Buso, Tiziana & Corgnati, Stefano Paolo, 2017. "A customized modelling approach for multi-functional buildings – Application to an Italian Reference Hotel," Applied Energy, Elsevier, vol. 190(C), pages 1302-1315.
    4. Krarti, Moncef & Aldubyan, Mohammad & Williams, Eric, 2020. "Residential building stock model for evaluating energy retrofit programs in Saudi Arabia," Energy, Elsevier, vol. 195(C).
    5. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    6. Johari, F. & Peronato, G. & Sadeghian, P. & Zhao, X. & Widén, J., 2020. "Urban building energy modeling: State of the art and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    7. Amin, Amin & Mourshed, Monjur, 2024. "Community stochastic domestic electricity forecasting," Applied Energy, Elsevier, vol. 355(C).
    8. Tooke, Thoreau Rory & van der Laan, Michael & Coops, Nicholas C., 2014. "Mapping demand for residential building thermal energy services using airborne LiDAR," Applied Energy, Elsevier, vol. 127(C), pages 125-134.
    9. Yanxia Li & Chao Wang & Sijie Zhu & Junyan Yang & Shen Wei & Xinkai Zhang & Xing Shi, 2020. "A Comparison of Various Bottom-Up Urban Energy Simulation Methods Using a Case Study in Hangzhou, China," Energies, MDPI, vol. 13(18), pages 1-23, September.
    10. Chen Xu & Yu Li & Xueting Jin & Liang Yuan & Hao Cheng, 2017. "A Real-Time Energy Consumption Simulation and Comparison of Buildings in Different Construction Years in the Olympic Central Area in Beijing," Sustainability, MDPI, vol. 9(12), pages 1-18, December.
    11. Bischof, Julian & Duffy, Aidan, 2022. "Life-cycle assessment of non-domestic building stocks: A meta-analysis of current modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    12. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    13. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    14. Omar Shafqat & Elena Malakhatka & Nina Chrobot & Per Lundqvist, 2021. "End Use Energy Services Framework Co-Creation with Multiple Stakeholders—A Living Lab-Based Case Study," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    15. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    16. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    17. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    18. Estiri, Hossein, 2014. "Building and household X-factors and energy consumption at the residential sector," Energy Economics, Elsevier, vol. 43(C), pages 178-184.
    19. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    20. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:8:y:2016:i:10:p:1063-:d:81123. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.