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AI and Expert Insights for Sustainable Energy Future

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  • Mir Sayed Shah Danish

    (Energy Systems (Chubu Electric Power) Funded Research Division, IMaSS (Institute of Materials and Systems for Sustainability), Nagoya University, Furocho, Chikusa Ward, Nagoya 464-8601, Aichi, Japan)

Abstract

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

Suggested Citation

  • Mir Sayed Shah Danish, 2023. "AI and Expert Insights for Sustainable Energy Future," Energies, MDPI, vol. 16(8), pages 1-27, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3309-:d:1118312
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    as
    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. Kok, M. & Lootsma, F. A., 1985. "Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model," European Journal of Operational Research, Elsevier, vol. 22(1), pages 44-55, October.
    3. Najib Rahman Sabory & Tomonobo Senjyu & Adina Hashemi Momand & Hadya Waqfi & Nilofar Saboor & Rabia Mobarez & Farida Razeqi, 2021. "LEED Scores of Residential Buildings in Poor Cities: Kabul City Case," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    4. Paweł Gładysz & Magdalena Strojny & Łukasz Bartela & Maciej Hacaga & Thomas Froehlich, 2022. "Merging Climate Action with Energy Security through CCS—A Multi-Disciplinary Framework for Assessment," Energies, MDPI, vol. 16(1), pages 1-28, December.
    5. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    6. Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
    7. Moncef Krarti, 2019. "Evaluation of Energy Efficiency Potential for the Building Sector in the Arab Region," Energies, MDPI, vol. 12(22), pages 1-45, November.
    8. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    9. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    10. GU, Jianqiang & Umar, Muhammad & Soran, Semih & Yue, Xiao-Guang, 2020. "Exacerbating effect of energy prices on resource curse: Can research and development be a mitigating factor?," Resources Policy, Elsevier, vol. 67(C).
    11. Nimisha Pandey & Heleen de Coninck & Ambuj D Sagar, 2022. "Beyond technology transfer: Innovation cooperation to advance sustainable development in developing countries," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(2), March.
    12. Irene Arcelay & Aitor Goti & Aitor Oyarbide-Zubillaga & Tugce Akyazi & Elisabete Alberdi & Pablo Garcia-Bringas, 2021. "Definition of the Future Skills Needs of Job Profiles in the Renewable Energy Sector," Energies, MDPI, vol. 14(9), pages 1-23, May.
    13. Li, Wei & Lu, Can & Zhang, Yan-Wu, 2019. "Prospective exploration of future renewable portfolio standard schemes in China via a multi-sector CGE model," Energy Policy, Elsevier, vol. 128(C), pages 45-56.
    14. Baptista, Marcia & Henriques, Elsa M.P. & de Medeiros, Ivo P. & Malere, Joao P. & Nascimento, Cairo L. & Prendinger, Helmut, 2019. "Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 228-239.
    15. Inoue, Nozomu & Matsumoto, Shigeru, 2019. "An examination of losses in energy savings after the Japanese Top Runner Program?," Energy Policy, Elsevier, vol. 124(C), pages 312-319.
    16. Hanane Thamik & Jiang Wu, 2022. "The Impact of Artificial Intelligence on Sustainable Development in Electronic Markets," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    17. Hui Li & Rou Li & Meng Shang & Yu Liu & Dandan Su, 2022. "Cooperative decisions of competitive supply chains considering carbon trading mechanism [Competing manufacturers in a retail supply chain: on contractual form and coordination]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 571-589.
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