Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
<|reference_start|>Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey: Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models that are based on statistical learning theory and are used as a tool to take use of the data that the power system and its users generate. Initially, we perform a thorough literature analysis of artificial intelligence (AI) applications related to renewable energy (RE). Next, we present a thorough analysis of renewable energy factories and assess their suitability, along with a list of the most widely used and appropriate AI algorithms. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This survey paper comprises an extensive review of the several AI techniques used for renewable energy as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of renewable energy. This literature review identifies the performance and outcomes of nine different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems. Additionally, it explored AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics- AI's significance in shaping the future of the energy industry. Furthermore, this article outlines future directions in the integration of AI for renewable energy.<|reference_end|>
@article{rashid2024present,
    title={Present and Future of AI in Renewable Energy Domain : A Comprehensive
  Survey},
    author={Abdur Rashid, Parag Biswas, Angona Biswas, MD Abdullah Al Nasim,
  Kishor Datta Gupta, Roy George},
    journal={arXiv preprint arXiv:2406.16965},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16965},
    primaryClass={cs.LG cs.AI cs.CY}
}