IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v93y2015ip2p1404-1412.html
   My bibliography  Save this article

Outdoor dry bulb heating design temperatures for Hungary

Author

Listed:
  • Verbai, Zoltán
  • Kocsis, Imre
  • Kalmár, Ferenc

Abstract

Saving energy is one of the main priorities in the building sector. In countries with temperate climates, heating represents an important share of the total energy use of buildings. It is well known that central heating systems operate most of the time at partial capacity during the heating season. Moreover, the elements of the central heating system are usually over dimensioned. In this paper, the outdoor dry bulb design temperatures for heating are analysed across Hungary. Using outdoor dry bulb temperature data from the last 50 years, cumulative frequency graphs were built and new design values are proposed at 99% and 99.5% confidence levels. For two typical residential buildings, a single family house and a block of flats, the consequences of the higher outdoor design temperature were analysed from the point of view of investment costs, seasonal boiler efficiency and intermittent operation. The investment cost decreased by approximately 10% for the large buildings, the seasonal efficiency of traditional boilers increased by approximately 0.6%, the seasonal efficiency of condensing boilers decreased by approximately 1.2%, and the energy savings from intermittent operation decreased by 2–6%.

Suggested Citation

  • Verbai, Zoltán & Kocsis, Imre & Kalmár, Ferenc, 2015. "Outdoor dry bulb heating design temperatures for Hungary," Energy, Elsevier, vol. 93(P2), pages 1404-1412.
  • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1404-1412
    DOI: 10.1016/j.energy.2015.10.050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215014188
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2015.10.050?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. He, Jiang & Hoyano, Akira & Asawa, Takashi, 2009. "A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance," Applied Energy, Elsevier, vol. 86(9), pages 1596-1605, September.
    2. Yang, Liu & Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "A new method to develop typical weather years in different climates for building energy use studies," Energy, Elsevier, vol. 36(10), pages 6121-6129.
    3. Bulut, Hüsamettin & Büyükalaca, Orhan & Yılmaz, Tuncay, 2003. "New outdoor heating design data for Turkey," Energy, Elsevier, vol. 28(12), pages 1133-1150.
    4. Verbai, Zoltán & Lakatos, Ákos & Kalmár, Ferenc, 2014. "Prediction of energy demand for heating of residential buildings using variable degree day," Energy, Elsevier, vol. 76(C), pages 780-787.
    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. Han, Ou & Li, Angui & Dong, Xinwei & Li, Jianwei, 2021. "Determination of HVAC meteorological parameters for floating nuclear power stations (FNPSs) in the area of China sea and its vicinity," Energy, Elsevier, vol. 233(C).
    2. Szodrai, Ferenc & Lakatos, Ákos & Kalmár, Ferenc, 2016. "Analysis of the change of the specific heat loss coefficient of buildings resulted by the variation of the geometry and the moisture load," Energy, Elsevier, vol. 115(P1), pages 820-829.

    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. Chung, Mo & Park, Hwa-Choon, 2015. "Comparison of building energy demand for hotels, hospitals, and offices in Korea," Energy, Elsevier, vol. 92(P3), pages 383-393.
    2. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    3. Ascione, Fabrizio & Bianco, Nicola & Rossi, Filippo de’ & Turni, Gianluca & Vanoli, Giuseppe Peter, 2012. "Different methods for the modelling of thermal bridges into energy simulation programs: Comparisons of accuracy for flat heterogeneous roofs in Italian climates," Applied Energy, Elsevier, vol. 97(C), pages 405-418.
    4. Kiluk, Sebastian, 2012. "Algorithmic acquisition of diagnostic patterns in district heating billing system," Applied Energy, Elsevier, vol. 91(1), pages 146-155.
    5. Putra, I Dewa Gede Arya & Nimiya, Hideyo & Sopaheluwakan, Ardhasena & Kubota, Tetsu & Lee, Han Soo & Pradana, Radyan Putra & Alfata, Muhammad Nur Fajri & Perdana, Reza Bayu & Permana, Donaldi Sukma & , 2024. "Development of typical meteorological years based on quality control of datasets in Indonesia," Renewable Energy, Elsevier, vol. 221(C).
    6. Li, Honglian & Huang, Jin & Hu, Yao & Wang, Shangyu & Liu, Jing & Yang, Liu, 2021. "A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China," Energy, Elsevier, vol. 231(C).
    7. Toparlar, Y. & Blocken, B. & Maiheu, B. & van Heijst, G.J.F., 2018. "Impact of urban microclimate on summertime building cooling demand: A parametric analysis for Antwerp, Belgium," Applied Energy, Elsevier, vol. 228(C), pages 852-872.
    8. Oktay, Z. & Coskun, C. & Dincer, I., 2011. "A new approach for predicting cooling degree-hours and energy requirements in buildings," Energy, Elsevier, vol. 36(8), pages 4855-4863.
    9. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    10. 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.
    11. Borreguero, Ana M. & Luz Sánchez, M. & Valverde, José Luis & Carmona, Manuel & Rodríguez, Juan F., 2011. "Thermal testing and numerical simulation of gypsum wallboards incorporated with different PCMs content," Applied Energy, Elsevier, vol. 88(3), pages 930-937, March.
    12. 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).
    13. Capeluto, I. Guedi & Ochoa, Carlos E., 2014. "Simulation-based method to determine climatic energy strategies of an adaptable building retrofit façade system," Energy, Elsevier, vol. 76(C), pages 375-384.
    14. 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.
    15. Dirks, James A. & Gorrissen, Willy J. & Hathaway, John H. & Skorski, Daniel C. & Scott, Michael J. & Pulsipher, Trenton C. & Huang, Maoyi & Liu, Ying & Rice, Jennie S., 2015. "Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach," Energy, Elsevier, vol. 79(C), pages 20-32.
    16. Szodrai, Ferenc & Lakatos, Ákos & Kalmár, Ferenc, 2016. "Analysis of the change of the specific heat loss coefficient of buildings resulted by the variation of the geometry and the moisture load," Energy, Elsevier, vol. 115(P1), pages 820-829.
    17. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    18. Kiluk, S., 2014. "Dynamic classification system in large-scale supervision of energy efficiency in buildings," Applied Energy, Elsevier, vol. 132(C), pages 1-14.
    19. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    20. Tian, Wei & Liu, Yunliang & Heo, Yeonsook & Yan, Da & Li, Zhanyong & An, Jingjing & Yang, Song, 2016. "Relative importance of factors influencing building energy in urban environment," Energy, Elsevier, vol. 111(C), pages 237-250.

    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:93:y:2015:i:p2:p:1404-1412. 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.