IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3309-d1118312.html
   My bibliography  Save this article

AI and Expert Insights for Sustainable Energy Future

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3309/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3309/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    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.
    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. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    2. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
    3. Yi Kuang & Xiuli Wang & Hongyang Zhao & Yijun Huang & Xianlong Chen & Xifan Wang, 2020. "Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm," Energies, MDPI, vol. 13(19), pages 1-20, September.
    4. Abdollah Younesi & Hossein Shayeghi & Pierluigi Siano, 2020. "Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids," Energies, MDPI, vol. 13(5), pages 1-22, March.
    5. Mir Sayed Shah Danish & Tomonobu Senjyu, 2023. "AI-Enabled Energy Policy for a Sustainable Future," Sustainability, MDPI, vol. 15(9), pages 1-16, May.
    6. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    7. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    8. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    9. Fábio T. F. Silva & Alexandre Szklo & Amanda Vinhoza & Ana Célia Nogueira & André F. P. Lucena & Antônio Marcos Mendonça & Camilla Marcolino & Felipe Nunes & Francielle M. Carvalho & Isabela Tagomori , 2022. "Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-39, October.
    10. A.P. Wierzbicki, 1998. "Reference Point Methods in Vector Optimization and Decision Support," Working Papers ir98017, International Institute for Applied Systems Analysis.
    11. Sun, J. & Wen, W. & Wang, M. & Zhou, P., 2022. "Optimizing the provincial target allocation scheme of renewable portfolio standards in China," Energy, Elsevier, vol. 250(C).
    12. Yating, Yang & Mughal, Nafeesa & Wen, Jun & Thi Ngan, Truong & Ramirez-Asis, Edwin & Maneengam, Apichit, 2022. "Economic performance and natural resources commodity prices volatility: Evidence from global data," Resources Policy, Elsevier, vol. 78(C).
    13. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    14. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.
    15. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    16. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    17. Dominique Barth & Benjamin Cohen-Boulakia & Wilfried Ehounou, 2022. "Distributed Reinforcement Learning for the Management of a Smart Grid Interconnecting Independent Prosumers," Energies, MDPI, vol. 15(4), pages 1-19, February.
    18. Khalid A. Alnowibet & Ahmad M. Alshamrani & Adel F. Alrasheedi, 2023. "A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power," Energies, MDPI, vol. 16(7), pages 1-15, April.
    19. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    20. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).

    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:jeners:v:16:y:2023:i:8:p:3309-:d:1118312. 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.