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Prediction of Energy Consumption in Residential Buildings Using Type-2 Fuzzy Wavelet Neural Network

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15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022 (ICAFS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 610))

Abstract

Residential buildings use a significant part of the total energy of the countries. The utilisation of energy is defined by consumer occupancy, construction materials used in buildings. The timely changes of these factors lead to vague and imprecise representations of energy consumption prediction. Fuzzy logic is a more suitable approach for modelling this problem. In this paper, type-2 fuzzy wavelet neural networks (T2-FWNN) is proposed for modelling the energy consumption prediction of residential buildings. The system implements type-2 fuzzy reasoning using wavelet neural network technology. A gradient descent algorithm using a cross-validation approach has been applied for the construction of T2-FWNN system. The learning of T2-FWNN system is based on an adaptive procedure that adjusts learning rates for stabilisation of training. The constructed system is used for the prediction of energy demand in residential buildings of Northern Cyprus. The presented comparative results prove the effectiveness of the constructed T2-FWNN model and the suitability of the T2-FWNN in the prediction of energy demand.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Near East University, Lefkosa, North Cyprus

    Rahib Abiyev

  2. Department of Electrical and Electronic Engineering, Near East University, Lefkosa, North Cyprus

    Sanan Abizada

Authors
  1. Rahib Abiyev
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  2. Sanan Abizada
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Corresponding author

Correspondence to Rahib Abiyev .

Editor information

Editors and Affiliations

  1. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    R. A. Aliev

  2. System Research Institute, Polish Academy of Sciences, Warsaw, Poland

    J. Kacprzyk

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    W. Pedrycz

  4. Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA

    Mo. Jamshidi

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    M. B. Babanli

  6. Department of Mechatronics, Near East University, Nicosia, Türkiye

    F. Sadikoglu

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Abiyev, R., Abizada, S. (2023). Prediction of Energy Consumption in Residential Buildings Using Type-2 Fuzzy Wavelet Neural Network. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M.B., Sadikoglu, F. (eds) 15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022. ICAFS 2022. Lecture Notes in Networks and Systems, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-031-25252-5_46

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