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The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

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  • Miltiadis D. Lytras

    (School of Business, Deree College—The American College of Greece, 153-42 Athens, Greece
    Effat College of Engineering, Effat University, P.O. Box 34689, Jeddah 21478, Saudi Arabia)

  • Kwok Tai Chui

    (Department of Technology, School of Science and Technology, The Open University of Hong Kong, Ho Man Tin, Kowloon, Hong Kong, China)

Abstract

Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue “Artificial Intelligence for Smart and Sustainable Energy Systems and Applications”, eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

Suggested Citation

  • Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3108-:d:257213
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    References listed on IDEAS

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    1. Hui He & Zixuan Liu & Runhai Jiao & Guangwei Yan, 2019. "A Novel Nonintrusive Load Monitoring Approach based on Linear-Chain Conditional Random Fields," Energies, MDPI, vol. 12(9), pages 1-17, May.
    2. Mahmoud, Tawfek & Dong, Z.Y. & Ma, Jin, 2018. "An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine," Renewable Energy, Elsevier, vol. 126(C), pages 254-269.
    3. Nicoletti, Jack & Ning, Chao & You, Fengqi, 2019. "Incorporating agricultural waste-to-energy pathways into biomass product and process network through data-driven nonlinear adaptive robust optimization," Energy, Elsevier, vol. 180(C), pages 556-571.
    4. Demir, Kubilay & Ismail, Hatem & Vateva-Gurova, Tsvetoslava & Suri, Neeraj, 2018. "Securing the cloud-assisted smart grid," International Journal of Critical Infrastructure Protection, Elsevier, vol. 23(C), pages 100-111.
    5. Zhenbing Zhao & Zhen Zhen & Lei Zhang & Yincheng Qi & Yinghui Kong & Ke Zhang, 2019. "Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN," Energies, MDPI, vol. 12(7), pages 1-15, March.
    6. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Ali & Tamer Moussa, 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks," Energies, MDPI, vol. 12(11), pages 1-15, June.
    7. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    8. Chen, G.Q. & Wu, X.F., 2017. "Energy overview for globalized world economy: Source, supply chain and sink," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 735-749.
    9. Cuce, Erdem & Harjunowibowo, Dewanto & Cuce, Pinar Mert, 2016. "Renewable and sustainable energy saving strategies for greenhouse systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 34-59.
    10. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    11. Ahmed Gowida & Salaheldin Elkatatny & Emad Ramadan & Abdulazeez Abdulraheem, 2019. "Data-Driven Framework to Predict the Rheological Properties of CaCl 2 Brine-Based Drill-in Fluid Using Artificial Neural Network," Energies, MDPI, vol. 12(10), pages 1-17, May.
    12. Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
    13. Zi-Jia Wang & Zhi-Hui Zhan & Jun Zhang, 2018. "Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation," Energies, MDPI, vol. 11(12), pages 1-14, December.
    14. Aqdas Naz & Muhammad Umar Javed & Nadeem Javaid & Tanzila Saba & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids," Energies, MDPI, vol. 12(5), pages 1-30, March.
    15. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
    16. Thi-Thu-Huong Le & Howon Kim, 2018. "Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate," Energies, MDPI, vol. 11(12), pages 1-35, December.
    17. Ning, Chao & You, Fengqi, 2019. "Data-driven Wasserstein distributionally robust optimization for biomass with agricultural waste-to-energy network design under uncertainty," Applied Energy, Elsevier, vol. 255(C).
    18. Muhammad Awais & Nadeem Javaid & Khursheed Aurangzeb & Syed Irtaza Haider & Zahoor Ali Khan & Danish Mahmood, 2018. "Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids," Energies, MDPI, vol. 11(11), pages 1-30, November.
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    1. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
    2. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
    5. Shin-Cheng Yeh & Ai-Wei Wu & Hui-Ching Yu & Homer C. Wu & Yi-Ping Kuo & Pei-Xuan Chen, 2021. "Public Perception of Artificial Intelligence and Its Connections to the Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-34, August.

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