Energy Consumption Prediction of Residential Buildings Using Fuzzy Neural Networks

  • Sanan Abizada
  • Esmira AbiyevaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)


This paper presents an energy consumption prediction model of residential buildings using fuzzy neural networks (FNN). The design of FNN prediction model has been performed using clustering and gradient descent algorithms. A cross-validation procedure is used for the training of the FNN. The descriptions of the training algorithms have been given. The statistical data is applied to design FNN. The obtained simulation results prove the effectiveness of using FNN in the energy consumption prediction of residential buildings. Based on prediction results of the energy consumption, the efficient ventilation system of the buildings can be planned. As a result, the energy waste can be decreased considerably.


Energy consumption Fuzzy neural networks Prediction 


  1. 1.
    Yu, Z., Haghigrat, F., Fung, B.C.M., Yoshimo, H.: A decision tree method for building energy demand modeling. Energy Build. 42, 1637–1646 (2010)CrossRefGoogle Scholar
  2. 2.
    Perez-Lombard, L., Ortiz, J., Pout, C.: A review on buildings energy consumption information. Energy Build. 40(3), 394–398 (2008)CrossRefGoogle Scholar
  3. 3.
    The Home of DOE2 based Building Energy Use and Cost Analysis Software (1998).
  4. 4.
    Strachan, P.A., Kokogiannakis, G., Macdonald, I.A.: History and development of validation with the ESP-r simulation program. Build. Environ. 43(4), 601–609 (2008)CrossRefGoogle Scholar
  5. 5.
    Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., Buhl, W.F., Huang, Y.J., Pedersen, C.O., Strand, R.K., Liesen, R.J., Fisher, D.E., Witte, M.J., Glazer, J.: EnergyPlus: creating anew-generation building energy simulation program. Energy Build. 33(4), 319–331 (2001)CrossRefGoogle Scholar
  6. 6.
    Yan, D., Xia, J., Tang, W., Song, F., Zhang, X., Jiang, Y.: DeST—an integrated building simulation toolkit part I: fundamentals. Build. Simul. 1(2), 95–110 (2008)CrossRefGoogle Scholar
  7. 7.
    Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools’. Energy Build. 49, 560–567 (2012)CrossRefGoogle Scholar
  8. 8.
    Platt, G., Li, J., Li, R., Poulton, G., James, G., Wal, J.: Adaptive HVAC zone modelling for sustainable buildings. Energy Build. 42, 412–421 (2010)CrossRefGoogle Scholar
  9. 9.
    Dong, B., Cao, C., Lee, S.E.: Applying support vector machines to predict building energy consumption in tropical region. Energy Build. 37, 545–553 (2005)CrossRefGoogle Scholar
  10. 10.
    Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A.: Applying support vector machine to predict hourly cooling load in the building. Appl. Energy 86, 2249–2256 (2009)CrossRefGoogle Scholar
  11. 11.
    Yezioro, A., Dong, B., Leite, F.: An applied artificial intelligence approach towards assessing building performance simulation tools. Energy Build. 40, 612–620 (2008)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Haghighat, F.: Development of artificial neural network based heat convection for thermal simulation of large rectangular cross-sectional area earth-to-earth heat exchanges. Energy Build. 42(4), 435–440 (2010)CrossRefGoogle Scholar
  13. 13.
    Schiavon, S., Lee, K.H., Bauman, F., Webster, T.: Influence of raised floor on zone design cooling load in commercial buildings. Energy Build. 42(8), 1182–1191 (2010)CrossRefGoogle Scholar
  14. 14.
    Wan, K.K.W., Li, H.W., Liu, D., Lam, J.C.: Future trends of building heating and cooling loads and energy consumption in different climates. Build. Environ. 46, 223–234 (2011)CrossRefGoogle Scholar
  15. 15.
    Tsanas, A., Goulermas, J.Y., Vartela, V., Tsiapras, D., Theodorakis, G., Fisher, A.C., Sfirakis, P.: The Windkessel model revisited: a qualitative analysis of the circulatory system. Med. Eng. Phys. 31, 581–588 (2009)CrossRefGoogle Scholar
  16. 16.
    Abiyev, R., Abiyev, V.H., Ardil, C.: Electricity consumption prediction model using neuro-fuzzy system. Int. J. Comput. Inf. Eng. 3(12), 2963–2966 (2009)Google Scholar
  17. 17.
    Abiyev, R.H.: Fuzzy wavelet neural network for prediction of electricity consumption. AIEDAM: Artif. Intell. Eng. Des. Anal. Manuf. 23(2), 09–118 (2009)CrossRefGoogle Scholar
  18. 18.
    Abiyev, R.H.: Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction. Neural Comput. Appl. 20(2), 249–259 (2011)CrossRefGoogle Scholar
  19. 19.
    Abiyev, R.H., Abiyev, V.H.: Differential evaluation learning of fuzzy wavelet neural networks for stock price prediction. J. Inf. Comput. Sci. 7(2), 121–130 (2012). ISSN 1746-7659, England, UKGoogle Scholar
  20. 20.
    Abiyev, R.H., Akkaya, N., Aytac, E., Günsel, I., Çağman, A.: Brain-computer interface for control of wheelchair using fuzzy neural networks. Biomed Res. Int. 2016, 1–9 (2016)CrossRefGoogle Scholar
  21. 21.
    Abiyev, R.H., Abizade, S.: Diagnosing Parkinson’s diseases using fuzzy neural system. Comput. Math. Methods Med. 2016, 1–9 (2016)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Abiyev, R.H.: Credit rating using type-2 fuzzy neural networks. Math. Probl. Eng. 2014, 1–8 (2014)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Abiyev R.H., Alshanableh T.: Neuro-fuzzy network for adaptive channel equalization. In: 5th Mexican International Conference on Artificial Intelligence, MICAI – 2006, pp. 13–17. IEEE CS press, Apizaco (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electrical and Electronic Engineering DepartmentNear East UniversityNicosiaTurkey
  2. 2.Economics DepartmentNear East UniversityNicosiaTurkey

Personalised recommendations