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An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

Author

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  • Vinothini Arumugham

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Hayder M. A. Ghanimi

    (Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq)

  • Denis A. Pustokhin

    (Department of Logistics, State University of Management, 109542 Moscow, Russia)

  • Irina V. Pustokhina

    (Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

  • Vidya Sagar Ponnam

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India)

  • Meshal Alharbi

    (Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia)

  • Parkavi Krishnamoorthy

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Sudhakar Sengan

    (Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India)

Abstract

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Suggested Citation

  • Vinothini Arumugham & Hayder M. A. Ghanimi & Denis A. Pustokhin & Irina V. Pustokhina & Vidya Sagar Ponnam & Meshal Alharbi & Parkavi Krishnamoorthy & Sudhakar Sengan, 2023. "An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5453-:d:1102177
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    References listed on IDEAS

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    1. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
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    3. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
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    Cited by:

    1. Abdullah Abonamah & Salah Hassan & Tena Cale, 2025. "Artificial Intelligence and Environmental Sustainability Playbook for Energy Sector Leaders," Sustainability, MDPI, vol. 17(14), pages 1-27, July.
    2. Xiu Ji & Mingge Li & Zheyu Yue & Haifeng Zhang & Yizhu Wang, 2024. "Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game," Energies, MDPI, vol. 18(1), pages 1-22, December.
    3. Irina Georgescu & Ayşe Meriç Yazıcı & Vildan Bayram & Mesut Öztırak & Ayşegül Toy & Mesut Dogan, 2025. "Governing the Green Transition: The Role of Artificial Intelligence, Green Finance, and Institutional Governance in Achieving the SDGs Through Renewable Energy," Sustainability, MDPI, vol. 17(12), pages 1-21, June.
    4. Arsalan Masood & Ubaid Ahmed & Syed Zulqadar Hassan & Ahsan Raza Khan & Anzar Mahmood, 2025. "Economic Value Creation of Artificial Intelligence in Supporting Variable Renewable Energy Resource Integration to Power Systems: A Systematic Review," Sustainability, MDPI, vol. 17(6), pages 1-42, March.
    5. Yang, Chengying & Wu, Zhixin & Li, Xuetao & Fars, Ashk, 2024. "Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles," Energy, Elsevier, vol. 288(C).
    6. Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.
    7. Maissa Taktak & Faouzi Derbel, 2025. "Evaluating the Impact of Frequency Decomposition Techniques on LSTM-Based Household Energy Consumption Forecasting," Energies, MDPI, vol. 18(10), pages 1-31, May.

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