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Deep Neural Network for Predicting Changing Market Demands in the Energy Sector for a Sustainable Economy

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Listed:
  • Mingming Wen

    (School of Management, Guangdong Ocean University, Zhanjiang 524088, China
    Guangdong Coastal Economic Belt Development Research Institute, Zhanjiang 524088, China)

  • Changshi Zhou

    (School of Management, Guangdong Ocean University, Zhanjiang 524088, China)

  • Mamonov Konstantin

    (Institute of Construction and Civil Engineering, O. M. Beketov National University of Urban Economy in Kharkiv, 17, Marshala Bazhanova St., 1002 Kharkiv, Ukraine)

Abstract

Increasing access to power, enhancing clean cooking fuels, decreasing wasteful energy subsidies, and limiting fatal air pollution are just a few of the sustainable development goals that all revolve around energy (E). Energy-specific sustainable development objectives were a turning point in the global shift towards a more sustainable and just system. By understanding energy resources, markets, regulations, and scientific studies, the country can progress more quickly towards a sustainable economy (SE). Investment in renewable energy industries is hampered by institutional obstacles such as market-controlled procedures and inconsistent supporting policies. Power plant building is currently incompatible with existing transmission and distribution networks, posing significant risks to investors. Deep neural networks (DNN) are specifically investigated in this article for energy demand forecasting at the individual building level. Other relevant information is supplied into fully connected layers along with the convolutional output. A single customer’s power usage data were used and analyzed for the final fuel and electricity consumption by various energy sources and consumer groups to test the DNN-SE technique. The energy intensity and labor productivity indexes for several economic sectors are displayed. A wide range of economic activities are examined to determine their impact on environmental pollution indicators, greenhouse gas emissions, and other air pollutants. A more effective and comprehensive energy efficiency strategy should be implemented to lower emission levels at lower prices. Research-based conclusions must be enhanced to help policymaking. The results of the experiment using the proposed method show that it is possible to predict 98.1%, grow at 96.8%, meet 98.5% of electricity demand, use 97.6% of power, and have a renewable energy ratio of 96.2%.

Suggested Citation

  • Mingming Wen & Changshi Zhou & Mamonov Konstantin, 2023. "Deep Neural Network for Predicting Changing Market Demands in the Energy Sector for a Sustainable Economy," Energies, MDPI, vol. 16(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2407-:d:1086126
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    References listed on IDEAS

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    1. Debabrata Barik & Arun M. & Muhammad Ahsan Saeed & Tholkappiyan Ramachandran, 2022. "Experimental and Computational Analysis of Aluminum-Coated Dimple and Plain Tubes in Solar Water Heater System," Energies, MDPI, vol. 16(1), pages 1-18, December.
    2. Ronggang Zhang & Sathishkumar V E & R. Dinesh Jackson Samuel, 2020. "Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    3. Mohsin, Muhammad & Hanif, Imran & Taghizadeh-Hesary, Farhad & Abbas, Qaiser & Iqbal, Wasim, 2021. "Nexus between energy efficiency and electricity reforms: A DEA-Based way forward for clean power development," Energy Policy, Elsevier, vol. 149(C).
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    1. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    2. Yang Tang & Yifeng Liu & Weiqiang Huo & Meng Chen & Shilong Ye & Lei Cheng, 2023. "Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach," Energies, MDPI, vol. 16(7), pages 1-22, March.

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