Abstract
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.
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Abizada, S., Abiyeva, E. (2019). Energy Consumption Prediction of Residential Buildings Using Fuzzy Neural Networks. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_68
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DOI: https://doi.org/10.1007/978-3-030-04164-9_68
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