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The Future of Machine Learning Algorithms for Renewable Energy Systems

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Several industries that will leverage machine learning's smart data processing, reinforcement learning, and other capabilities are renewable energy sources. Today, machine learning (ML) and artificial intelligence (AI) provide energy enterprises with a significant choice of making more efficient and valuable operations to increase capital growth and promote the transition to renewable energy. ML can enhance progressive outcomes in the energy industry and domain carbon reduction that is becoming a strong emphasis. Emerging ML algorithms can design and maintain a futuristic renewable energy system that is smart and uses as many renewable energy sources as is financially feasible and societally welcomed. ML can offer realistic and helpful forecasts for when a residential or developing will be capable of creating energy but then when this will have to take energy from the utility according to the seasons and period of the day. As precision increases, this may become more beneficial as a management technique in the future. The deep learning-powered industry forecast model can anticipate wind and solar power production up to 30 days ahead of time. Physical object tracking that automatically follows the sunlight and set the solar panels to maximize the quantity of energy they create during the day is also aided by ML.

Editors

  • Dr. Iskander Tlili Dr. Iskander Tlili , 

    Dr. Iskander Tlili

    ISKANDER TLILI received the M.Sc. and Ph.D degrees in thermal energy at the Laboratory Studies of Thermal and Energy Systems LESTE, National Engineering School of Monastir, Tunisia. He is currently an Associate Professor. He has more than 18 years of teaching and research experience in thermo-fluid, thermal power, renewable energy and desalination. He participated in the implementation of several energy audits. He has published many papers in highly reputed journals. He has been conferred with internal and external grants from different international sponsors and has led several units and committees at both national and international level.

  • Dr. Ahmed Awan Dr. Ahmed Awan  &

    Dr. Ahmed Awan

    AHMED BILAL AWAN received the B.Sc. degree in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan, in 2004, the master’s degree from L’École Supérieure d’Electricité (SUPELEC), Paris, France, in 2007, and the Ph.D. degree from the University de Lorraine, Nancy, France, in 2011. He is currently working as an Assistant Professor with the Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates. His main research interests include renewable energy, solar energy, distributed power generation.

  • Dr. Sa'ed Awni Musmar Dr. Sa'ed Awni Musmar

    Dr. Sa'ed Awni Musmar

    Sa'ed Awni Musmar is a Professor of industrial Engineering at the University of Jordan and act as a chairman of Industrial Engineering Department who graduated from Materials and metallurgical department at McGill University in Sep. 2006 and since then he worked at mechanical and/or industrial engineering departments at Mu'tah University, Almajmaah University (KSA) and the University of Jordan. Fellowships and grants were related to aluminum fluidity enhancement, energy conversion in industrial plants, materials science synchrotron applications and Black Carbon Recycling.

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