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Investigating Energy Use in a City District in Nordic Climate Using Energy Signature

Author

Listed:
  • Martin Eriksson

    (Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 801 76 Gävle, Sweden)

  • Jan Akander

    (Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 801 76 Gävle, Sweden)

  • Bahram Moshfegh

    (Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, University of Gävle, 801 76 Gävle, Sweden
    Division of Energy Systems, Department of Management and Engineering, Linköping University, 581 83 Linköping, Sweden)

Abstract

This paper focuses on multi-family buildings in a Swedish city district, erected between 1965 and 1973, which are now in need of renovation. For the two types of multi-family buildings in the district, tower buildings and low-rise buildings, dynamic energy use is predicted by using an energy signature method. The energy signature is then used to calculate the primary energy use number of the building stock, according to calculations methods dictated by Swedish building regulations. These regulations are also used to assess which multi-family buildings are in need of renovation, based on the buildings’ primary energy use. For buildings that need energy renovations, it is simulated so that the energy use of each multi-family building complies with these same building regulations. The proposed methodology for simulating energy renovation also determines new energy signature parameters, related to building heat loss coefficient, balance temperature and domestic hot water usage. The effects of simulated renovation are displayed in a duration diagram, revealing how a large-scale renovation affects the district’s heat load in different annual periods, which affects the local district heating system. Sensitivity analysis is also performed before and after simulated energy renovation.

Suggested Citation

  • Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1907-:d:764715
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    as
    1. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    2. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    3. Delzendeh, Elham & Wu, Song & Lee, Angela & Zhou, Ying, 2017. "The impact of occupants’ behaviours on building energy analysis: A research review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1061-1071.
    4. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    5. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
    6. Christoffer Rasmussen & Peder Bacher & Davide Calì & Henrik Aalborg Nielsen & Henrik Madsen, 2020. "Method for Scalable and Automatised Thermal Building Performance Documentation and Screening," Energies, MDPI, vol. 13(15), pages 1-23, July.
    7. Nageler, P. & Heimrath, R. & Mach, T. & Hochenauer, C., 2019. "Prototype of a simulation framework for georeferenced large-scale dynamic simulations of district energy systems," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Park, Somin & Shim, Jisoo & Song, Doosam, 2021. "Issues in calculation of balance-point temperatures for heating degree-days for the development of building-energy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
    10. Difs, Kristina & Bennstam, Marcus & Trygg, Louise & Nordenstam, Lena, 2010. "Energy conservation measures in buildings heated by district heating – A local energy system perspective," Energy, Elsevier, vol. 35(8), pages 3194-3203.
    11. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
    12. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    13. Gustafsson, Mattias & Rönnelid, Mats & Trygg, Louise & Karlsson, Björn, 2016. "CO2 emission evaluation of energy conserving measures in buildings connected to a district heating system – Case study of a multi-dwelling building in Sweden," Energy, Elsevier, vol. 111(C), pages 341-350.
    14. Anti Hamburg & Targo Kalamees, 2018. "The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use," Energies, MDPI, vol. 11(11), pages 1-15, November.
    15. Charlier, Dorothée, 2021. "Explaining the energy performance gap in buildings with a latent profile analysis," Energy Policy, Elsevier, vol. 156(C).
    16. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    17. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    18. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    19. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    20. Sovacool, Benjamin K. & Cabeza, Luisa F. & Pisello, Anna Laura & Colladon, Andrea Fronzetti & Larijani, Hatef Madani & Dawoud, Belal & Martiskainen, Mari, 2021. "Decarbonizing household heating: Reviewing demographics, geography and low-carbon practices and preferences in five European countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    21. 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.
    22. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    23. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    24. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    25. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    26. 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.
    27. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    1. Piotr Michalak & Krzysztof Szczotka & Jakub Szymiczek, 2023. "Audit-Based Energy Performance Analysis of Multifamily Buildings in South-East Poland," Energies, MDPI, vol. 16(12), pages 1-21, June.
    2. Sukjoon Oh & John F. Gardner, 2022. "Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners," Sustainability, MDPI, vol. 14(14), pages 1-19, July.

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