1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam
2Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu - 602105, India
3Mechanical Engineering Department, Politeknik Negeri Padang, West Sumatera, Indonesia
4 Institute of Maritime, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam
5 Faculty of Automotive Engineering, Dong A University, Danang, Viet Nam, Viet Nam
6 Institute of Mechanical Engineering, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam
BibTex Citation Data :
@article{IJRED60387, author = {Thanh Tuan Le and Prabhu Paramasivam and Elvis Adril and Van Quy Nguyen and Minh Xuan Le and Minh Thai Duong and Huu Cuong Le and Anh Quan Nguyen}, title = {Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms}, journal = {International Journal of Renewable Energy Development}, volume = {13}, number = {4}, year = {2024}, keywords = {Machine learning; Artificial Intelligence; Renewable energy; Waste-to-energy path; Sustainable energy}, abstract = { This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects }, pages = {783--813} doi = {10.61435/ijred.2024.60387}, url = {https://ijred.cbiore.id/index.php/ijred/article/view/60387} }
Refworks Citation Data :
This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects
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