IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1270-d791533.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1270/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1270/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
    2. Svetlana Vladislavlevna Lobova & Aleksei Valentinovich Bogoviz & Yulia Vyacheslavovna Ragulina & Alexander Nikolaevich Alekseev, 2019. "The Fuel and Energy Complex of Russia: Analyzing Energy Efficiency Policies at the Federal Level," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 205-211.
    3. Mac Clay, Pablo & Börner, Jan & Sellare, Jorge, 2023. "Institutional and macroeconomic stability mediate the effect of auctions on renewable energy capacity," Energy Policy, Elsevier, vol. 180(C).
    4. Du, Kun & Calautit, John & Eames, Philip & Wu, Yupeng, 2021. "A state-of-the-art review of the application of phase change materials (PCM) in Mobilized-Thermal Energy Storage (M-TES) for recovering low-temperature industrial waste heat (IWH) for distributed heat," Renewable Energy, Elsevier, vol. 168(C), pages 1040-1057.
    5. Mohammad Mafizur Rahman & Nahid Sultana, 2024. "Nexus of Human Development and Environmental Quality in Low-Income and Developing Countries: Do Renewable Energy and Good Governance Matter?," Sustainability, MDPI, vol. 16(13), pages 1-18, June.
    6. Wadim Strielkowski & Anna Sherstobitova & Patrik Rovny & Tatiana Evteeva, 2021. "Increasing Energy Efficiency and Modernization of Energy Systems in Russia: A Review," Energies, MDPI, vol. 14(11), pages 1-19, May.
    7. Yukun Xu & Xiangyong Kong & Zheng Zhu & Chao Jiang & Shuang Xiao, 2022. "Recovery Algorithm of Power Metering Data Based on Collaborative Fitting," Energies, MDPI, vol. 15(4), pages 1-19, February.
    8. Shaikh Zishan & Altaf Hossain Molla & Haroon Rashid & Kok Hoe Wong & Ahmad Fazlizan & Molla Shahadat Hossain Lipu & Mohd Tariq & Omar Mutab Alsalami & Mahidur R. Sarker, 2023. "Comprehensive Analysis of Kinetic Energy Recovery Systems for Efficient Energy Harnessing from Unnaturally Generated Wind Sources," Sustainability, MDPI, vol. 15(21), pages 1-18, October.
    9. Sampene, Agyemang Kwasi & Li, Cai & Wiredu, John, 2024. "An outlook at the switch to renewable energy in emerging economies: The beneficial effect of technological innovation and green finance," Energy Policy, Elsevier, vol. 187(C).
    10. Khribich, Abir & Kacem, Rami H. & Dakhlaoui, Ahlem, 2021. "Causality nexus of renewable energy consumption and social development: Evidence from high-income countries," Renewable Energy, Elsevier, vol. 169(C), pages 14-22.
    11. Umut Uzar, 2022. "The connection between freedom of the press and environmental quality: An investigation on emerging market countries," Natural Resources Forum, Blackwell Publishing, vol. 46(1), pages 21-38, February.
    12. Tinta, Abdoulganiour Almame, 2023. "Energy substitution in Africa: Cross-regional differentiation effects," Energy, Elsevier, vol. 263(PA).
    13. Angelina A. Kolomeytseva & Maria A. Maksakova, 2019. "Integration Potential in Energy Sector: Eurasian Economic Union Case," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 174-181.
    14. Ding, Qian & Huang, Jianbai & Chen, Jinyu & Tao, Dali, 2023. "Internet development and renewable energy technological innovation: Does institutional quality matter?," Renewable Energy, Elsevier, vol. 218(C).
    15. Kandasamy, Jeevitha & Ramachandran, Rajeswari & Veerasamy, Veerapandiyan & Irudayaraj, Andrew Xavier Raj, 2024. "Distributed leader-follower based adaptive consensus control for networked microgrids," Applied Energy, Elsevier, vol. 353(PA).
    16. Abbas, Khizar & Han, Mengyao & Xu, Deyi & Butt, Khalid Manzoor & Baz, Khan & Cheng, Jinhua & Zhu, Yongguang & Hussain, Sanwal, 2024. "Exploring synergistic and individual causal effects of rare earth elements and renewable energy on multidimensional economic complexity for sustainable economic development," Applied Energy, Elsevier, vol. 364(C).
    17. Talan, Amogh & Rao, Amar & Sharma, Gagan Deep & Apostu, Simona-Andreea & Abbas, Shujaat, 2023. "Transition towards clean energy consumption in G7: Can financial sector, ICT and democracy help?," Resources Policy, Elsevier, vol. 82(C).
    18. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    19. Ofori, Elvis K. & Bekun, Festus V. & Gyamfi, Bright Akwasi & Ali, Ernest B. & Onifade, Stephen T. & Asongu, Simplice A., 2024. "Prospect of trade and innovation in renewable energy deployment: A comparative analysis between BRICS and MINT Countries," Renewable Energy, Elsevier, vol. 229(C).
    20. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1270-:d:791533. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.