Abstract
The estimation of the energy performance of residential buildings has gained importance because of the significant consumption of electricity in housing estate areas. For this aim, different approaches were utilized for robust and accurate prediction of the energy load in buildings. The use of different kind of construction materials, timely change in building parameters lead to imprecise and vague evaluation of energy consumption. For such kind of problems that are characterized with uncertainties, the use of fuzzy set theory is a more suitable approach for the modeling of energy consumption. This paper proposes a novel type-2 fuzzy wavelet neural network (T2FWNN) for modeling the energy performance of residential buildings. Based on the type-2 fuzzy rules, the multi-input multi-output T2FWNN model is proposed. For the construction of the T2FWNN model, the learning algorithm has been designed using cross-validation approach, clustering and gradient descent algorithms. During construction, the adaptive learning procedure was developed to stabilize and speed up the learning process. The proposed model is used for the solution of two problems. At the first stage, based on statistical data, the T2FWNN model has been designed for modeling the cooling and heating load of residential buildings. In the second stage, using T2FWNN the prediction model was designed for the energy consumption of residential buildings in Northern Cyprus. Comparative results have been provided to prove the efficiency of using the designed model in the prediction of the energy load of residential buildings. The obtained results indicated the suitability of using the T2FWNN system for estimation of the energy performance and prediction of the energy consumption of residential buildings.
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Abiyev, R., Abizada, S. Type-2 fuzzy wavelet neural network for estimation energy performance of residential buildings. Soft Comput 25, 11175–11190 (2021). https://doi.org/10.1007/s00500-021-05873-4
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DOI: https://doi.org/10.1007/s00500-021-05873-4