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

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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)

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.

Suggested Citation

  • Mohsen Ahmadi & Mahsa Soofiabadi & Maryam Nikpour & Hossein Naderi & Lazim Abdullah & Behdad Arandian, 2022. "Developing a Deep Neural Network with Fuzzy Wavelets and Integrating an Inline PSO to Predict Energy Consumption Patterns in Urban Buildings," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1270-:d:791533
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    References listed on IDEAS

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    1. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    2. Uzar, Umut, 2020. "Political economy of renewable energy: Does institutional quality make a difference in renewable energy consumption?," Renewable Energy, Elsevier, vol. 155(C), pages 591-603.
    3. Mohsen Ahmadi, 2021. "A Computational Approach to Uncovering Economic Growth Factors," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1051-1076, December.
    4. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
    5. Elvir M. Akhmetshin & Sergey I. Kopylov & Svetlana V. Lobova & Natalya B. Panchenko & G. Kostyleva, 2018. "Specifics of the Fuel and Energy Complex Regulation: Seeking New Opportunities for Russian and International Aspects," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 169-177.
    6. Ashouri, Milad & Fung, Benjamin C.M. & Haghighat, Fariborz & Yoshino, Hiroshi, 2020. "Systematic approach to provide building occupants with feedback to reduce energy consumption," Energy, Elsevier, vol. 194(C).
    7. Amar Kumar Barik & Dulal Chandra Das & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2021. "Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning," Energies, MDPI, vol. 14(10), pages 1-26, May.
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