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Cost and Energy Assessment of Buildings Thermal Protection Level

  • Maksim Terekh
  • Darya TretyakovaEmail author
  • Nadezhda Morozova
  • Jurģis Zemītis
Conference paper
  • 138 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 70)

Abstract

In this article, two types of mathematical models for thermal protection level analysis were developed: the economic and energy ones. Both of which allow to calculate the relevant thickness of the selected insulation material under any climatic and economic conditions with any constant layers of building envelope taken from structural considerations. The factors influencing the models were also evaluated. The main factors on which the economic model depends are the costs of energy and insulation material in the region, the region degree-days, the discount rate and the billing period. The key factors to influence the energy model are the region degree-days and the energy consumption rate for the production, transportation and installation of the insulation material. Comparing the two models developed, the following conclusion can be made: The economic model is practically easier to use, as all of the necessary information could be found in the standards, and in the manufacturer catalogs, it allows to get a more precise and accurate result, while the energy approach requires the data, which sometimes has no public access. But, energy model provides us with a more objective assessment when comparing the level of building thermal protection in different countries.

Keywords

Energy efficiency Thermal resistance Optimal insulation Energy savings Thermal insulating Life cycle assessment 

References

  1. 1.
    Danilevsky, L.N., Danilevsky, S.L.: The algorithm and accuracy of definition of heattechnical indicators of buildings. Mag. Civ. Eng. 73(5), 49–61 (2017)Google Scholar
  2. 2.
    Statcenko, E.A., Ostrovaia, A.F., Musorina, T.A., Kukolev, M.I., Petritchenko, M.R.: The elementary mathematical model of sustainable enclosing structure. Mag. Civ. Eng. 68(8), 86–91 (2016)CrossRefGoogle Scholar
  3. 3.
    Kisilewicz, T.: On the role of external walls in the reduction of energy demand and the mitigation of human thermal discomfort. MDPI Sustain. 1–20 (2019)Google Scholar
  4. 4.
    Gabbar, H.A., Musharavati, F., Pokharel, S.: System approach for building energy conservation. In: 6th International Conference on Sustainability in Energy and Buildings, SEB-14 System 2014, Energy Procedia, vol. 62, pp. 666–675 (2014)Google Scholar
  5. 5.
    Shao, B., Liu, X.: Calculation of energy saving based on building engineering. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 837–844 (2020)Google Scholar
  6. 6.
    Gorshkov, A.S., Sokolov, N.A.: Inconsistency in Russian and international standards in the determination of the design values of thermal conductivity of building materials and products. Mag. Civ. Eng. 42(7), 7–14 (2013)CrossRefGoogle Scholar
  7. 7.
    Knat’ko, M.V., Yefimenko, M.N., Gorshkov, A.S.: K voprosu o dolgovechnosti i energoeffektivnosti sovremennykh ograzhdayushchikh stenovykh konstruktsiy zhilykh, administrativnykh i proizvodstvennykh zdaniy [On the issue of durability and energy efficiency of modern enclosing wall structures of residential, administrative and industrial buildings]. Mag. Civ. Eng. 2(2), 50–53 (2008)Google Scholar
  8. 8.
    Gorshkov, A.S., Vatin, N.I., Rymkevich, P.P., Kydrevich, O.O.: Payback period of investments in energy saving. Mag. Civ. Eng. 78(2), 65–75 (2018)Google Scholar
  9. 9.
    Aditya, L., Mahlia, T.M.I., Rismanchi, B., Ng, H.M., Hasan, H.M., Metselaar, H.S.C., Muraza, O., Aditiya, H.B.: A review on insulation materials for energy conservation in buildings. Renew. Sustain. Energy Rev. 73, 1352–1365 (2017)CrossRefGoogle Scholar
  10. 10.
    Zhang, L., Liu, Z., Hou, C., Hou, J., Wei, D., Hou, Y.: Case Studies in thermal engineering optimization analysis of thermal insulation layer attributes of building envelope exterior wall based on DeST and life cycle economic evaluation. Case Stud. Therm. Eng. 14, 1–9 (2019)Google Scholar
  11. 11.
    Averyanova, O.V.: Economic efficiency of energy saving solutions. Mag. Civ. Eng. 23(5), 53–59 (2011)CrossRefGoogle Scholar
  12. 12.
    Zekic-Susac, M., Knezevic, M., Scitovski, R.: Deep learning in modeling energy cost of buildings in the public sector. In: International Workshop on Soft Computing Models in Industrial and Environmental Applications, pp. 101–110 (2020)Google Scholar
  13. 13.
    Morano, P., Rosato, P., Tajani, F., Di Liddo F.: An analysis of the energy efficiency impacts on the residential property prices in the City of Bari. Values and Functions for Future Cities, pp. 73–88 (2019)Google Scholar
  14. 14.
    Olaussen, J.O., Oust, A., Solstad, J.T., Kristiansen L.: Energy performance certificates—the role of the energy price. MDPI. Energies, 1–14 (2019)Google Scholar
  15. 15.
    Dylewski, R.: Optimal Thermal Insulation Thicknesses of External Walls Based on Economic and Ecological Heating Cost. MDPI Energies, pp. 1–14 (2019)Google Scholar
  16. 16.
    Alsayed, M.F., Tayeh. R.A.: Life cycle cost analysis for determining optimal insulation thickness in palestinian buildings. J. Build. Eng. 22, 101–112 (2019)Google Scholar
  17. 17.
    Ozel, M.: Thermal performance and optimum insulation thickness of building walls with different structure materials. Appl. Therm. Eng. 31, 3854–3863 (2011)CrossRefGoogle Scholar
  18. 18.
    Feng, W., Huang, J., Lv, H., Guo, D., Huang, Z.: Environmental effects determination of the economical insulation thickness of building envelopes simultaneously in energy-saving renovation of existing residential buildings. Energy Sources, Part A: Recover., Util., Environ. Eff. 41, 665–676 (2019)CrossRefGoogle Scholar
  19. 19.
    Tamene, Y., Serir, L.: Thermal and economic study on building external walls for improving energy efficiency. Int. J. Heat Technol. 37(1), 219–228 (2019)CrossRefGoogle Scholar
  20. 20.
    Zolotova, I.Y.: Prognozirovanie roznichnykh tsen na elektroenergiiu uchet regionalnykh osobennostei tsenoobrazovaniia na primere regionov iuzhnogo federalnogo okruga [Forecasting retail prices for electric power: taking into account regional features of pricing on the example of regions of the southern federal district]. Energeticheskaia politika 5, 109–119 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Maksim Terekh
    • 1
  • Darya Tretyakova
    • 1
    Email author
  • Nadezhda Morozova
    • 1
  • Jurģis Zemītis
    • 2
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySt. PetersburgRussian Federation
  2. 2.Riga Technical UniversityRigaLatvia

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