IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p7988-d1297067.html
   My bibliography  Save this article

AI-Driven Urban Energy Solutions—From Individuals to Society: A Review

Author

Listed:
  • Kinga Stecuła

    (Faculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Radosław Wolniak

    (Faculty of Organization and Management, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Wieslaw Wes Grebski

    (Penn State Hazleton, Pennsylvania State University, 76 University Drive, Hazleton, PA 18202-8025, USA)

Abstract

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI’s role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption.

Suggested Citation

  • Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7988-:d:1297067
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/7988/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/24/7988/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed A. Eltawil & Maged Mohammed & Nayef M. Alqahtani, 2023. "Developing Machine Learning-Based Intelligent Control System for Performance Optimization of Solar PV-Powered Refrigerators," Sustainability, MDPI, vol. 15(8), pages 1-35, April.
    2. Png, Ethan & Srinivasan, Seshadhri & Bekiroglu, Korkut & Chaoyang, Jiang & Su, Rong & Poolla, Kameshwar, 2019. "An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings," Applied Energy, Elsevier, vol. 239(C), pages 408-424.
    3. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    4. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    5. Viktor Koval & Oksana Borodina & Iryna Lomachynska & Piotr Olczak & Anzor Mumladze & Dominika Matuszewska, 2022. "Model Analysis of Eco-Innovation for National Decarbonisation Transition in Integrated European Energy System," Energies, MDPI, vol. 15(9), pages 1-19, May.
    6. Vidura Sumanasena & Lakshitha Gunasekara & Sachin Kahawala & Nishan Mills & Daswin De Silva & Mahdi Jalili & Seppo Sierla & Andrew Jennings, 2023. "Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation," Energies, MDPI, vol. 16(5), pages 1-18, February.
    7. Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.
    8. Aburas, Marina & Soebarto, Veronica & Williamson, Terence & Liang, Runqi & Ebendorff-Heidepriem, Heike & Wu, Yupeng, 2019. "Thermochromic smart window technologies for building application: A review," Applied Energy, Elsevier, vol. 255(C).
    9. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    10. Jarosław Kulpa & Paweł Kamiński & Kinga Stecuła & Dariusz Prostański & Piotr Matusiak & Daniel Kowol & Michał Kopacz & Piotr Olczak, 2021. "Technical and Economic Aspects of Electric Energy Storage in a Mine Shaft—Budryk Case Study," Energies, MDPI, vol. 14(21), pages 1-14, November.
    11. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    12. JinHyo Joseph Yun & Xiaofei Zhao & Lei Ma & Zheng Liu & Bo Yang, 2023. "Open innovation dynamics and evolution in the mobile payment industry – comparative analysis among Daegu, Cardiff, and Nanjing," European Planning Studies, Taylor & Francis Journals, vol. 31(5), pages 862-884, 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. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Coordination of resources at the edge of the electricity grid: Systematic review and taxonomy," Applied Energy, Elsevier, vol. 318(C).
    2. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    5. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    6. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    7. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    8. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    9. Mukherjee, Monish & Hardy, Trevor & Fuller, Jason C. & Bose, Anjan, 2022. "Implementing multi-settlement decentralized electricity market design for transactive communities with imperfect communication," Applied Energy, Elsevier, vol. 306(PA).
    10. Anatoliy M. Pavlenko & Karolina Sadko, 2023. "Evaluation of Numerical Methods for Predicting the Energy Performance of Windows," Energies, MDPI, vol. 16(3), pages 1-23, February.
    11. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    12. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Integrated prosumers–DSO approach applied in peer-to-peer energy and reserve tradings considering network constraints," Applied Energy, Elsevier, vol. 317(C).
    13. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    14. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    15. Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
    16. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    17. Yang, Sungwoong & Cho, Hyun Mi & Yun, Beom Yeol & Hong, Taehoon & Kim, Sumin, 2021. "Energy usage and cost analysis of passive thermal retrofits for low-rise residential buildings in Seoul," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Jungwon Yoon & Sanghyun Bae, 2020. "Performance Evaluation and Design of Thermo-Responsive SMP Shading Prototypes," Sustainability, MDPI, vol. 12(11), pages 1-35, May.
    19. Jayachandran, M. & Rao, K. Prasada & Gatla, Ranjith Kumar & Kalaivani, C. & Kalaiarasy, C. & Logasabarirajan, C., 2022. "Operational concerns and solutions in smart electricity distribution systems," Utilities Policy, Elsevier, vol. 74(C).
    20. Michalis Michael & Fabio Favoino & Qian Jin & Alessandra Luna-Navarro & Mauro Overend, 2023. "A Systematic Review and Classification of Glazing Technologies for Building Façades," Energies, MDPI, vol. 16(14), pages 1-47, July.

    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:24:p:7988-:d:1297067. 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.