IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v25y2000i5p479-491.html
   My bibliography  Save this article

Artificial neural networks for the prediction of the energy consumption of a passive solar building

Author

Listed:
  • Kalogirou, Soteris A.
  • Bojic, Milorad

Abstract

Artificial neural networks (ANNs) have been used for the prediction of the energy consumption of a passive solar building. The building structure consists of one room with an inclined roof. Two cases were investigated, an all insulated building and a building with one wall made completely of masonry and the other walls made partially of masonry and thermal insulation. The investigation was performed for two seasons: winter, for which the building with the masonry-only wall is facing south, and summer, for which the building with the masonry-only wall is facing north. The building's thermal behaviour was evaluated by using a dynamic thermal building model constructed on the basis of finite volumes and time marching. The energy consumption of the building depends on whether all walls have insulation, on the thickness of the masonry and insulation and on the season. Simulated data for a number of cases were used to train an artificial neural network (ANN) in order to generate a mapping between the above easily measurable inputs and the desired output, i.e., the building energy consumption in kWh. The simulated buildings had walls varying from 15 cm to 60 cm in thickness. The objective of this work is to produce another simulation program, using ANNs, to model the thermal behaviour of the building. A multilayer recurrent architecture using the standard back-propagation learning algorithm has been applied. The results obtained for the training set are such that they yield a coefficient of multiple determination (R2 value) equal to 0.9985. The network was used subsequently for predictions of the energy consumption for cases other than the ones used for training. The coefficient of multiple determination obtained in this case was equal to 0.9991, which is very satisfactory. The ANN model proved to be much faster than the dynamic simulation programs.

Suggested Citation

  • Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    • Handle: RePEc:eee:energy:v:25:y:2000:i:5:p:479-491
    DOI: 10.1016/S0360-5442(99)00086-9
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544299000869
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0360-5442(99)00086-9?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. Bojić, M. & Lukić, N., 2000. "Numerical evaluation of solar-energy use through passive heating of weekend houses in Yugoslavia," Renewable Energy, Elsevier, vol. 20(2), pages 207-222.
    2. Clarke, J.A. & Strachan, P.A., 1994. "Simulation of conventional and renewable building energy systems," Renewable Energy, Elsevier, vol. 5(5), pages 1178-1189.
    3. Kalogirou, Soteris A. & Neocleous, Constantinos C. & Schizas, Christos N., 1998. "Artificial neural networks for modelling the starting-up of a solar steam-generator," Applied Energy, Elsevier, vol. 60(2), pages 89-100, June.
    Full references (including those not matched with items on IDEAS)

    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. Lukić, N., 2005. "The transient house heating condition—the daily changes of the building envelope response factor (BER)," Renewable Energy, Elsevier, vol. 30(4), pages 537-549.
    2. Lukić, N., 2003. "The transient house heating condition—the building envelope response factor (BER)," Renewable Energy, Elsevier, vol. 28(4), pages 523-532.
    3. Bojić, M. & Lukić, N., 2000. "Numerical evaluation of solar-energy use through passive heating of weekend houses in Yugoslavia," Renewable Energy, Elsevier, vol. 20(2), pages 207-222.
    4. Saari, Arto & Kalamees, Targo & Jokisalo, Juha & Michelsson, Rasmus & Alanne, Kari & Kurnitski, Jarek, 2012. "Financial viability of energy-efficiency measures in a new detached house design in Finland," Applied Energy, Elsevier, vol. 92(C), pages 76-83.
    5. Bojić, M., 2000. "Optimization of heating and cooling of a building by employing refuse and renewable energy," Renewable Energy, Elsevier, vol. 20(4), pages 453-465.
    6. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    7. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    8. Gómez-Muñoz, Victor M. & Porta-Gándara, Miguel Angel, 2003. "Simplified architectural method for the solar control optimization of awnings and external walls in houses in hot and dry climates," Renewable Energy, Elsevier, vol. 28(1), pages 111-127.
    9. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    10. Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    11. Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
    12. Ferruzza, Davide & Kærn, Martin Ryhl & Haglind, Fredrik, 2020. "A method to account for transient performance requirements in the design of steam generators for concentrated solar power applications," Applied Energy, Elsevier, vol. 269(C).

    More about this item

    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:eee:energy:v:25:y:2000:i:5:p:479-491. 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.