Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings

Mohsen Ahmadi, Mahsa Soofiabadi, Maryam Nikpour, Hossein Naderi, Lazim Abdullah and Behdad Arandian
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Mohsen Ahmadi: Department of Industrial Engineering, Urmia University of Technology, Urmia 5716693188, Iran
Mahsa Soofiabadi: School of Architecture Urban Planning Construction Engineering, Polytechnic University of Milan, 29121 Piacenza, Italy
Maryam Nikpour: Department of Architecture, Ahvaz Branch, Islamic Azad University, Ahvaz 6134937333, Iran
Hossein Naderi: Department of Construction Engineering and Management, Pars University, Tehran 1413915361, Iran
Lazim Abdullah: Management Science Research Group, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
Behdad Arandian: Department of Electrical Engineering, Dolatabad Branch, Islamic Azad University, Isfahan 8341875185, Iran

Mathematics, 2022, vol. 10, issue 8, 1-17

Abstract: Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models. This non-linear relationship between these indicators and energy demand has led researchers to search for intelligent solutions, such as artificial neural networks for non-linear modeling and prediction. The purpose of this study was to use a deep neural network with fuzzy wavelets to predict energy demand in Iran. For the training of the presented components, a hybrid training method incorporating both an inline PSO and a gradient-based algorithm is presented. The provided technique predicts energy consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021. This study shows that the presented method provides high-performance prediction at a lower level of complexity.

Keywords: energy consumption; urban building; fuzzy logic; wavelet; inline PSO; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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