Renewable Energy Articles
Knowledge Graph: Renewable Energy. Author: Milena Jael SILVA. Ecolonical LAB.

Renewable Energy: Technological, Social, and Environmental Perspectives (2022-2024)

This corpus, comprising studies published from 2022 to 2024, provides a comprehensive overview of the diverse technological, social, and environmental dimensions of renewable energy deployment. From the development of more efficient energy storage systems to the integration of hybrid microgrids and the optimization of renewable energy generation through advanced algorithms, these works delve into the cutting-edge advancements shaping the future of sustainable energy.

Beyond technological innovations, this collection also addresses the crucial social and ecological impacts of renewable energy adoption. Topics such as community engagement in energy cooperatives, gender perspectives in the energy sector, and the environmental effects of wind farms are explored in detail, emphasizing the multifaceted nature of the renewable energy transition. These studies underscore the importance of inclusive and equitable energy policies that not only promote technological growth but also ensure social justice and environmental sustainability.

The integration of data analytics, artificial intelligence (AI), and big data into renewable energy systems represents a growing frontier in the optimization and management of energy resources. By leveraging AI-driven analytics, energy providers can forecast demand more accurately, optimize resource allocation, and enhance the overall efficiency of renewable energy networks.

By examining both the advancements and challenges associated with renewable energy, this body of research highlights the complexities involved in transitioning to a greener, more resilient energy future. It serves as a vital resource for stakeholders, policymakers, and researchers working toward achieving global sustainability goals through the widespread adoption of renewable energy technologies.

Main Topics Explored:

  • Technological Advancements in Energy Storage: Innovative energy storage systems, such as lithium-ion and solid-state batteries, are enhancing efficiency and reliability, enabling more consistent integration of renewable energy into grids.
  • Development of Hybrid Microgrids: Studies highlight the implementation of hybrid microgrids, combining solar, wind, and energy storage, as a decentralized solution for enhancing grid resilience in remote areas.
  • Optimization of Renewable Energy Generation: Research focuses on algorithms and machine learning models that optimize the performance and output of renewable energy systems, ensuring maximum efficiency and sustainability.
  • AI and Big Data in Renewable Energy: The integration of AI and big data analytics is driving smarter energy management, allowing for predictive maintenance, energy demand forecasting, and optimization of renewable resource distribution.
  • Community Engagement in Energy Cooperatives: Case studies illustrate the role of local communities in managing and benefiting from renewable energy projects through cooperative models, fostering greater social ownership and participation.
  • Gender Perspectives in Renewable Energy: Several works examine the role of women in the renewable energy sector, emphasizing gender equality as a key factor for advancing energy transitions and inclusive development.
  • Environmental Impacts of Renewable Energy Projects: Environmental studies assess the impacts of large-scale renewable projects, such as wind farms, on ecosystems and biodiversity, urging the need for careful environmental management in energy planning.
  • Policy and Regulatory Challenges: Research explores the barriers posed by existing policy frameworks, recommending reforms to better align with the rapid evolution of renewable energy technologies and markets.

2024

  1. Abbas, Ghulam; Zhi, Wu; Ali, Aamir. (2024). A two-stage reactive power optimization method for distribution networks based on a hybrid model and data-driven approach. IET Renewable Power Generation. https://doi.org/10.1049/rpg2.13096
  2. Adnan, Muhammad; Xiao, Baohua; Ali, Muhammad Ubaid; et al. (2024). Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking. Environmental Challenges, 16. https://doi.org/10.1016/j.envc.2024.100976
  3. Ajagekar, Akshay; You, Fengqi. (2024). Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization. Advances in Applied Energy, 14. https://doi.org/10.1016/j.adapen.2024.100179
  4. Alaerjan, Alaa; Jabeur, Randa; Ben Chikha, Haithem; et al. (2024). Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods. Symmetry, 16(4). https://doi.org/10.3390/sym16040459
  5. Alhamrouni, Ibrahim; Abdul Kahar, Nor Hidayah; Salem, Mohaned; et al. (2024). A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions. Applied Sciences (Switzerland), 14(14). https://doi.org/10.3390/app14146214
  6. Allal, Zaid; Noura, Hassan N.; Vernier, Flavien; et al. (2024). Wind turbine fault detection and identification using a two-tier machine learning framework. Intelligent Systems with Applications, 22. https://doi.org/10.1016/j.iswa.2024.200372
  7. Angioni, Simone; Consoli, Sergio; Dessi, Danilo; et al. (2024). Exploring Environmental, Social, and Governance (ESG) Discourse in News: An AI-Powered Investigation Through Knowledge Graph Analysis. IEEE Access, 12, 77269–77283. https://doi.org/10.1109/ACCESS.2024.3407188
  8. Anthony, Bokolo. (2024). Decentralized AIoT based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach. Cities, 152. https://doi.org/10.1016/j.cities.2024.105198
  9. Atwa, Walid; Almazroi, Abdulwahab Ali; Ayub, Nasir. (2024). Reliable renewable energy forecasting for climate change mitigation. PeerJ Computer Science, 10, 1–25. https://doi.org/10.7717/PEERJ-CS.2067
  10. Awogbemi, Omojola; Kallon, Daramy Vandi Von; Kumar, K. Sunil. (2024). Contributions of artificial intelligence and digitization in achieving clean and affordable energy. Intelligent Systems with Applications, 22. https://doi.org/10.1016/j.iswa.2024.200389
  11. Awasthi, Saurabh; Singh, Gagan; Ahamad, Nafees. (2024). Classifying Electrical Faults in a Distribution System Using K-Nearest Neighbor (KNN) Model in Presence of Multiple Distributed Generators. Journal of The Institution of Engineers (India): Series B, 105(3), 621–634. https://doi.org/10.1007/s40031-024-00994-4
  12. Balcıoğlu, Yavuz Selim; Çelik, Ahmet Alkan; Altındağ, Erkut. (2024). Artificial Intelligence Integration in Sustainable Business Practices: A Text Mining Analysis of USA Firms. Sustainability (Switzerland), 16(15). https://doi.org/10.3390/su16156334
  13. Balaji Nanda Kumar Reddy, K.; Pratyusha, D.; Sravanthi, B.; et al. (2024). Recent AI Applications in Electrical Vehicles for Sustainability. SSRG International Journal of Mechanical Engineering, 11(3), 50–64. https://doi.org/10.14445/23488360/IJME-V11I3P106
  14. Bhandary, Aakash; Dobariya, Vruti; Yenduri, Gokul; et al. (2024). Enhancing Household Energy Consumption Predictions Through Explainable AI Frameworks. IEEE Access, 12, 36764–36777. https://doi.org/10.1109/ACCESS.2024.3373552
  15. Bouaouda, Amine; Afdel, Karim; Abounacer, Rachida. (2024). Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI. Sustainability (Switzerland), 16(11). https://doi.org/10.3390/su16114438
  16. Cairone, Stefano; Hasan, Shadi W.; Choo, Kwang-Ho; et al. (2024). Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies. Journal of Water Process Engineering, 63. https://doi.org/10.1016/j.jwpe.2024.105486
  17. Campos, Filipe D.; Sousa, Tiago C.; Barbosa, Ramiro S. (2024). Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM. Energies, 17(11). https://doi.org/10.3390/en17112582
  18. Damasevicius, Robertas; Jovanovic, Luka; Petrovic, Aleksandar; et al. (2024). Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation. PeerJ Computer Science, 10. https://doi.org/10.7717/peerj-cs.1795
  19. De Gioannis, Elena; Dudka, Aurore; Łapniewska, Zofia. Gender stereotypes and empowerment of women in energy cooperatives: A comparative analysis from Italy and Belgium. Energy Research and Social Science, 2024, vol. 116. https://doi.org/10.1016/j.erss.2024.103673.
  20. Dormoy, Emeric; Le Lostec, Brice; Haillot, Didier. Aboveground compressed air energy storage systems: Experimental and numerical approach. Energy Conversion and Management, 2024, vol. 321. https://doi.org/10.1016/j.enconman.2024.119073.
  21. Emami, Alireza; Araújo, Rui; Cruz, Sérgio; et al. (2024). A systematic approach to modeling synchronous generator using Markov parameters and Takagi–Sugeno fuzzy systems. Expert Systems with Applications, 235. https://doi.org/10.1016/j.eswa.2023.121122
  22. Erekath, Swathi; Seidlitz, Holger; Schreiner, Monika; Dreyer, Christian. (2024). Food for future: Exploring cutting-edge technology and practices in vertical farm. Sustainable Cities and Society, 106. https://doi.org/10.1016/j.scs.2024.105357
  23. Eslava-Zapata, Rolando; Sánchez-Castillo, Verenice; Juaneda-Ayensa, Emma. (2024). Key players in renewable energy and artificial intelligence research. EAI Endorsed Transactions on Energy Web, 11, 1–10. https://doi.org/10.4108/ew.5182
  24. Gayathry, V.; Kaliyaperumal, Deepa; Salkuti, Surender Reddy. (2024). Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-68531-3
  25. Giovanardi, Matteo; Baietta, Alessia; Belletti, Francesco; et al. (2024). Exploiting the Value of Active and Multifunctional Façade Technology through the IoT and AI. Applied Sciences (Switzerland), 14(3). https://doi.org/10.3390/app14031145
  26. Hasan, Mohammad Kamrul; Ahmed, Musse Mohamud; Islam, Shayla; et al. (2024). Malaysia energy outlook from 1990 to 2050 for sustainability: Business-as-usual and Alternative-policy Scenarios based economic projections with AI based experiments. Energy Strategy Reviews, 53. https://doi.org/10.1016/j.esr.2024.101360
  27. Hassan, Marwa; Beshr, Eman. (2024). Predicting soil cone index and assessing suitability for wind and solar farm development using machine learning techniques. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-52702-3
  28. Hosseinpour, Majid; Noori, Meysam; Shahparasti, Mahdi. A 17-level octuple boost switched-capacitor inverter with lower voltage stress on devices. Scientific Reports, 2024, vol. 14, no. 1. https://doi.org/10.1038/s41598-024-65211-0.
  29. Juma, Shaibu Ali; Ayeng’o, Sarah Paul; Kimambo, Cuthbert Z. M. (2024). A review of control strategies for optimized microgrid operations. IET Renewable Power Generation. https://doi.org/10.1049/rpg2.13056
  30. Kamagaté, Yaya; Shah, Heli Amit. Effective dynamic energy management algorithm for grid-interactive microgrid with hybrid energy storage system. Scientific Reports, 2024, vol. 14, no. 1. https://doi.org/10.1038/s41598-024-70599-w.
  31. Khasawneh, Hussam J.; Ghazal, Zaid A.; Al-Khatib, Waseem M.; et al. (2024). Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2024.2323818
  32. Krikser, Thomas; Ehlers, Melf-Hinrich; Profeta, Adriano. Municipal heat provision experiences and expectations in Germany. Energy, Sustainability and Society, 2024, vol. 14, no. 1. https://doi.org/10.1186/s13705-023-00433-0.
  33. Lan, Sha; Deprizon; Razmjooy, Navid. (2024). Enhancing the performance of zero energy buildings with boosted coyote optimization and elman neural networks. Energy Reports, 11, 5214–5226. https://doi.org/10.1016/j.egyr.2024.05.001
  34. Lee, Jooseung; Kang, Jimyung; Lee, Soonwoo; Oh, Hui-Myoung. (2024). Ultra-Short Term Photovoltaic Generation Forecasting Based on Data Decomposition and Customized Hybrid Model Architecture. IEEE Access, 12, 20840–20853. https://doi.org/10.1109/ACCESS.2024.3362234
  35. Liu, Haoran; Li, Huaqiang; Yu, Xueying; et al. (2024). A load classification method based on data augmentation and few-shot machine learning. IET Renewable Power Generation. https://doi.org/10.1049/rpg2.13029
  36. Liu, Luyao; Shen, Xinwei; Chen, Zhigang; et al. (2024). Optimal Energy Management of Data Center Micro-Grid Considering Computing Workloads Shift. IEEE Access, 12, 102061–102075. https://doi.org/10.1109/ACCESS.2024.3432120
  37. Markom, Arni Munira; Yusof, Yusrina; Markom, Marni Azira; et al. (2024). Digital technologies evolution in swiftlet farming: a systematic literature review. International Journal of Electrical and Computer Engineering, 14(4), 4456–4470. https://doi.org/10.11591/ijece.v14i4.pp4456-4470
  38. Martinez, Manuel Jose. Critical evaluation of transit policies in Lima, Peru; resilience of rail rapid transit (Metro) in a developing country. Green Energy and Intelligent Transportation, 2024, vol. 3, no. 5. https://doi.org/10.1016/j.geits.2024.100172.
  39. Meher, Jitendra Kumar; Rizvi, Syed Haider Abbas; Choudhary, Bhramar; et al. (2024). Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach. Energies, 17(12). https://doi.org/10.3390/en17122913
  40. Miraftabzadeh, Seyed Mahdi; Longo, Michela; Di Martino, Andrea; et al. (2024). Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles. Electronics (Switzerland), 13(10). https://doi.org/10.3390/electronics13101973
  41. Mohanty, Prasant Kumar; Hemant Kumar Reddy, K.; Panigrahy, Saroj Kumar; et al. (2024). Leveraging Generative & Explainable AI for Electric Vehicle Energy towards Sustainable, Consumer-Centric Transportation. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3405959
  42. Nagapurkar, Prashant; Nandy, Paulomi; Nimbalkar, Sachin. (2024). Cleaner Chips: Decarbonization in Semiconductor Manufacturing. Sustainability (Switzerland), 16(1). https://doi.org/10.3390/su16010218
  43. Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; et al. (2024). Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy. International Journal on Informatics Visualization, 8(1), 55–78. https://doi.org/10.62527/joiv.8.1.2637
  44. Nikulins, Arturs; Sudars, Kaspars; Edelmers, Edgars; et al. (2024). Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production. Energies, 17(5). https://doi.org/10.3390/en17051053
  45. Nie, Yuhao; Zelikman, Eric; Scott, Andea; et al. (2024). SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT. Advances in Applied Energy, 14. https://doi.org/10.1016/j.adapen.2024.100172
  46. Nleya, Sindiso Mpenyu; Velempini, Mthulisi. (2024). Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges. Applied Sciences (Switzerland), 14(13). https://doi.org/10.3390/app14135736
  47. Oteng, Clement; Obeng, Camara Kwasi; Gamette, Pius. Achieving carbon neutrality in an emerging oil-producing country: Renewable energy transition and international trade flows in Ghana. Research in Globalization, 2024, vol. 9. https://doi.org/10.1016/j.resglo.2024.100244.
  48. Pikus, Michał; Wąs, Jarosław. (2024). Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations. Energies, 17(3). https://doi.org/10.3390/en17030628
  49. Raihan, Asif. The interrelationship amid carbon emissions, tourism, economy, and energy use in Brazil. Carbon Research, 2024, vol. 3, no. 1. https://doi.org/10.1007/s44246-023-00084-y.
  50. Ramadan, Rabie A. (2024). Internet of things dataset for home renewable energy management. Data in Brief, 53. https://doi.org/10.1016/j.dib.2024.110166
  51. Rashed, Ahmed Nabih Zaki; et al. (2024). Connected smart elevator systems for smart power and time saving. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-69173-1
  52. Rojek, Izabela; Mikołajewski, Dariusz; Mroziński, Adam; Macko, Marek. (2024). Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review. Electronics (Switzerland), 13(16). https://doi.org/10.3390/electronics13163338
  53. Saeed, Syed Salman; Siraj, Md. Tanvir. Global Renewable Energy Infrastructure: Pathways to Carbon Neutrality and Sustainability. Solar Energy and Sustainable Development, 2024, vol. 13, no. 2, pp. 183–203. https://doi.org/10.51646/jsesd.v13i2.243.
  54. Song, Hui; Liu, Chen; Amani, Ali Moradi; et al. (2024). Smart optimization in battery energy storage systems: An overview. Energy and AI, 17. https://doi.org/10.1016/j.egyai.2024.100378
  55. Taghizadeh, Mehdi; Khayambashi, Kamiar; Hasnat, Md Abul; Alemazkoor, Negin. Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty. Electric Power Systems Research, 2024, vol. 237. https://doi.org/10.1016/j.epsr.2024.111014.
  56. Ueda, Daiju; Walston, Shannon L.; Fujita, Shohei; et al. (2024). Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future. Diagnostic and Interventional Imaging. https://doi.org/10.1016/j.diii.2024.06.002
  57. Ukoba, Kingsley; et al. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy and Environment. https://doi.org/10.1177/0958305X241256293
  58. Unsal, Derya Betul; Aksoz, Ahmet; et al. (2024). A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey. Sustainability (Switzerland), 16(7). https://doi.org/10.3390/su16072894
  59. van Klingeren, Fijnanda; De Moor, Tine. Ecological, financial, social and societal motives for cooperative energy prosumerism: measuring preference heterogeneity in a Belgian energy cooperative. Energy, Sustainability and Society, 2024, vol. 14, no. 1. https://doi.org/10.1186/s13705-024-00444-5.
  60. Vaish, Jayati; et al. (2024). Multi-objective optimization of distributed energy resources based microgrid using random forest model. Bulletin of Electrical Engineering and Informatics, 13(1), 67–75. https://doi.org/10.11591/eei.v13i1.7087
  61. Vinueza-Martinez, Jorge; et al. (2024). Geographic Information Systems (GISs) Based on WebGIS Architecture: Bibliometric Analysis of the Current Status and Research Trends. Sustainability (Switzerland), 16(15). https://doi.org/10.3390/su16156439
  62. Wang, Chongqing; Che, Yanhong; Xia, Mingqian; et al. (2024). The Evolution and Future Directions of Green Buildings Research: A Scientometric Analysis. Buildings, 14(2). https://doi.org/10.3390/buildings14020345
  63. Wei, Zhudeng; Wang, Minyan; Gao, Yang; Yu, Wenzheng; Jia, Aiqing; You, Zhiyuan; Wang, Zhihong. Offshore wind farm operation contributed to a slight improvement in seawater quality along the Jiangsu Coast, China. Scientific Reports, 2024, vol. 14, no. 1. https://doi.org/10.1038/s41598-024-71450-y.
  64. Xie, Tong; Wan, Yuwei; Zhou, Yufei; et al. (2024). Creation of a structured solar cell material dataset and performance prediction using large language models. Patterns, 5(5). https://doi.org/10.1016/j.patter.2024.100955
  65. Zhang, Jiahao; Cheng, Lan; Yang, Zhile; et al. (2024). An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids. Energy and AI, 17. https://doi.org/10.1016/j.egyai.2024.100377
  66. Zhang, Jiyun; et al. (2024). Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies. Accounts of Chemical Research, 57(9), 1434–1445. https://doi.org/10.1021/acs.accounts.4c00095
  67. Zhao, Lijun; et al. (2024). An Intelligent Web-based Energy Management System for Distributed Energy Resources Integration and Optimization. Journal of Web Engineering, 23(1), 165–196. https://doi.org/10.13052/jwe1540-9589.2316
  68. Zjavka, Ladislav. (2024). Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data. Systems Science and Control Engineering, 12(1). https://doi.org/10.1080/21642583.2024.2395400

2023

  1. Acaru, S. F.; Abdullah, R.; Lai, D. T. C.; Lim, R. C. (2023). Enhancing glucose classification in continuous flow hydrothermal biomass liquefaction streams through generative AI and IR spectroscopy. Energy Advances, 2(8), 1204–1214. https://doi.org/10.1039/d3ya00236e
  2. Alfaris, F. E. (2023). A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units. Energies, 16(3). https://doi.org/10.3390/en16031287
  3. Almasoudi, F. M. (2023). Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models. Sustainability (Switzerland), 15(10). https://doi.org/10.3390/su15108348
  4. Back, S.; Aspuru-Guzik, A.; Ceriotti, M.; et al. (2023). Accelerated chemical science with AI. Digital Discovery, 3(1), 23–33. https://doi.org/10.1039/d3dd00213f
  5. Bacanin, N.; Jovanovic, L.; Zivkovic, M.; et al. (2023). Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks. Information Sciences, 642. https://doi.org/10.1016/j.ins.2023.119122
  6. Buehler, K.; Kaufmann, K.; Patzold, M.; et al. (2023). Identifying defects on solar cells using magnetic field measurements and artificial intelligence trained by a finite-element-model. EPJ Photovoltaics, 14, 872–1064. https://doi.org/10.1051/epjpv/2023005
  7. Cai, G.; Jiang, C.; Yang, D.; et al. (2023). Data-driven predictive based load frequency robust control of power system with renewables. International Journal of Electrical Power and Energy Systems, 154. https://doi.org/10.1016/j.ijepes.2023.109429
  8. Chen, Y.; Bhutta, M. S.; Abubakar, M.; et al. (2023). Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM. Sustainability (Switzerland), 15(11). https://doi.org/10.3390/su15118555
  9. Cheung, M. C. J.; Yip, W. L.; Li, C. Y.; et al. (2023). Integrated Self-Sustained Renewable-Energy Explorer (iSEE). Journal of Asian Energy Studies, 7, 132–139. https://doi.org/10.24112/jaes.070010
  10. Dahmani, A.; Ammi, Y.; Bailek, N.; et al. (2023). Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction. Computers, Materials and Continua, 77, 2579–2594. https://doi.org/10.32604/cmc.2023.040625
  11. Das, K. P.; Chandra, J. (2023). A survey on artificial intelligence for reducing the climate footprint in healthcare. Energy Nexus, 9. https://doi.org/10.1016/j.nexus.2022.100167
  12. Dzogbewu, T. C.; de Beer, D. J. (2023). Additive manufacturing of selected ecofriendly energy devices. Virtual and Physical Prototyping, 18(1). https://doi.org/10.1080/17452759.2023.2276245
  13. Egbemhenghe, A. U.; Ojeyemi, T.; Iwuozor, K. O.; et al. (2023). Revolutionizing water treatment, conservation, and management: Harnessing the power of AI-driven ChatGPT solutions. Environmental Challenges, 13. https://doi.org/10.1016/j.envc.2023.100782
  14. Fan, Z.; Yan, Z.; Wen, S. (2023). Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813493
  15. Franki, V.; Majnarić, D.; Višković, A. (2023). A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector. Energies, 16(3). https://doi.org/10.3390/en16031077
  16. Gao, Y.; Wang, S.; Dragicevic, T.; et al. (2023). Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems. IEEE Open Journal of Industry Applications, 4, 366–375. https://doi.org/10.1109/OJIA.2023.3338534
  17. Gochhait, S.; Sharma, D. K. (2023). Regression Model-Based Short-Term Load Forecasting for Load Dispatch Center. Journal of Applied Engineering and Technological Science, 4(2), 693–710. https://doi.org/10.37385/jaets.v4i2.1682
  18. Guo, W.; Qureshi, N. M. F.; Jarwar, M. A.; et al. (2023). AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities. Sustainable Energy Technologies and Assessments, 56. https://doi.org/10.1016/j.seta.2022.102990
  19. Gutierrez-Rojas, D.; Narayanan, A.; Santos Nunes Almeida, C. R.; et al. (2023). A perspective on the enabling technologies of explainable AI-based industrial packetized energy management. iScience, 26(12). https://doi.org/10.1016/j.isci.2023.108415
  20. Hasanin, T.; Manoharan, H.; Alterazi, H. A.; et al. (2023). Mathematical approach of fiber optics for renewable energy sources using general adversarial networks. Frontiers in Ecology and Evolution, 11. https://doi.org/10.3389/fevo.2023.1132678
  21. Kahwash, F.; Barakat, B.; Maheri, A. (2023). Coupled thermo-electrical dispatch strategy with AI forecasting for optimal sizing of grid-connected hybrid renewable energy systems. Energy Conversion and Management, 293. https://doi.org/10.1016/j.enconman.2023.117460
  22. Lee, D. K.; Yoon, S. H.; Kwak, J. H.; et al. (2023). Sustainable Smart City Building-energy Management Based on Reinforcement Learning and Sales of ESS Power. KSII Transactions on Internet and Information Systems, 17(4), 1123–1146. https://doi.org/10.3837/tiis.2023.04.005
  23. Li, X.; Zhang, X.; Lin, F.; et al. (2023). Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full ZVS Range and Optimal Efficiency. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11(6), 5569–5581. https://doi.org/10.1109/JESTPE.2022.3185090
  24. Liu, Y.; Wang, Y.; Wang, Q.; et al. (2023). Recent advances in data-driven prediction for wind power. Frontiers in Energy Research, 11. https://doi.org/10.3389/fenrg.2023.1204343
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