Authors : Vinay Shrimali; Dr. Mukesh Shrimali

Volume/Issue : Volume 9 - 2024, Issue 11 - November

---

Google Scholar : https://tinyurl.com/547ryutb

Scribd : https://tinyurl.com/35btb867

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV828

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : The integration of Artificial Intelligence (AI) in the renewable energy sector offers transformative potential in optimizing energy production, distribution, and consumption. This paper explores the application of AI technologies in renewable energy, including solar, wind, hydroelectric, and energy storage systems. Key applications discussed include predictive analytics, energy management systems, smart grids, and optimization algorithms. We also review current challenges such as data quality, scalability, and regulatory barriers, and offer insights into future research directions.

References :

1. Zhao, X., et al. (2020). "Artificial Intelligence in Renewable Energy Systems: A Review of the State-of-the-Art."
   Renewable and Sustainable Energy Reviews, 120, 109608.
   [DOI: 10.1016/j.rser.2019.109608]

Summary: This review paper discusses the current state of AI applications in renewable energy systems, including energy forecasting, optimization, and smart grid management.

2. Liu, Y., et al. (2019). "Artificial Intelligence for Renewable Energy Systems: A Survey."
   Renewable and Sustainable Energy Reviews, 101, 147-157.
   [DOI: 10.1016/j.rser.2018.11.025]
   Summary: A comprehensive survey on the applications of AI in optimizing renewable energy systems, including solar, wind, and energy storage.

3. Gómez, L., et al. (2020). "Artificial Intelligence in Solar Energy Systems: Current and Future Applications."
   Journal of Energy Engineering, 146(4), 04020033.
   [DOI: 10.1061/(ASCE)EY.1943-7897.0000722]
   Summary: This paper reviews the application of AI technologies in solar energy systems, including energy forecasting, performance monitoring, and fault detection.
4. Islam, S., et al. (2019). "Data-driven Solar Power Forecasting Using Machine Learning Algorithms."
   Applied Energy, 237, 1150-1162.
   [DOI: 10.1016/j.apenergy.2018.12.001]
   Summary: The study explores machine learning models for solar power forecasting, which is essential for integrating solar energy into smart grids.
5. Tang, J., et al. (2020). "AI-based Predictive Maintenance for Wind Turbines: A Review."
   Renewable and Sustainable Energy Reviews, 129, 109916.
   [DOI: 10.1016/j.rser.2020.109916]
   Summary: This paper reviews predictive maintenance techniques for wind turbines using AI, including fault detection and operational optimization.
6. Zhao, W., et al. (2021). "Machine Learning Approaches for Wind Power Forecasting: A Comprehensive Review."
   Renewable and Sustainable Energy Reviews, 141, 110779.
   [DOI: 10.1016/j.rser.2021.110779]
   Summary: This review focuses on machine learning approaches to improve wind power forecasting accuracy and system integration.
7. Zhou, H., et al. (2020). "Artificial Intelligence in Smart Grid: A Review of Applications, Challenges, and Future Trends."
   Energy, 202, 117781.
   [DOI: 10.1016/j.energy.2020.117781]
   Summary: A review of how AI is being implemented in smart grids for optimal energy distribution, demand-side management, and grid stability.
8. Hosseini, S. S., et al. (2019). "AI-based Smart Grid Systems for Optimal Energy Distribution: A Review."
   Energy Reports, 5, 876-887.
   [DOI: 10.1016/j.egyr.2019.07.009]
   Summary: This paper provides a survey of AI algorithms used for managing and optimizing energy flow in smart grids, particularly in the context of renewable energy integration.

9. Xu, T., et al. (2020). "Artificial Intelligence in Battery Energy Storage Systems: A Review."
   Renewable and Sustainable Energy Reviews, 131, 109986.
   [DOI: 10.1016/j.rser.2020.109986]
   Summary: The paper reviews AI techniques applied to battery energy storage systems, focusing on performance prediction, optimization, and battery life management.
10. Yu, S., et al. (2020). "Artificial Intelligence-based Management of Energy Storage Systems for Renewable Energy Integration."
    Energy Storage Materials, 26, 561-573.
    [DOI: 10.1016/j.ensm.2019.09.017]
    Summary: This article discusses AI-based strategies for the effective management of energy storage systems to support renewable energy integration.
11. Cai, J., et al. (2020). "AI for Energy Efficiency: Applications in Buildings, Industry, and Transportation."
    Applied Energy, 274, 115233.
    [DOI: 10.1016/j.apenergy.2020.115233]
    Summary: A review on the application of AI in optimizing energy efficiency in various sectors, including residential, industrial, and transportation.
12. Chowdhury, M. S., et al. (2020). "AI-based Home Energy Management Systems: A Review."
    Renewable and Sustainable Energy Reviews, 133, 110176.
    [DOI: 10.1016/j.rser.2020.110176]
    Summary: This paper explores AI-based systems for managing energy use in homes, with a focus on integrating renewable energy sources like solar.
13. Liu, J., et al. (2021). "A Comprehensive Review of Machine Learning in Renewable Energy Forecasting and Optimization."
    Renewable and Sustainable Energy Reviews, 135, 110112.
    [DOI: 10.1016/j.rser.2020.110112]
    Summary: This paper focuses on the use of machine learning and AI for forecasting renewable energy generation and optimizing energy storage and grid operations.
14. Rashid, U., et al. (2021). "AI-based Optimization of Renewable Energy Systems for Smart Cities."
    Sustainable Cities and Society, 69, 102823.
    [DOI: 10.1016/j.scs.2021.102823]
    Summary: This study investigates the integration of AI for optimizing renewable energy in the context of smart cities, including energy storage and demand response.

15. Liu, Z., et al. (2019). "Case Study on the Integration of AI with Renewable Energy Systems in the European Union."
    Renewable Energy, 139, 1-10.
    [DOI: 10.1016/j.renene.2019.02.070]
    Summary: This paper presents real-world case studies of AI applications in renewable energy across various EU countries, demonstrating the effectiveness of these technologies.
16. Parnia, F., et al. (2020). "AI-Powered Smart Grid: Case Studies from Asia and Africa."
    Journal of Renewable and Sustainable Energy, 12(2), 025101.
    [DOI: 10.1063/1.5144381]
    Summary: This paper reviews case studies in developing countries where AI has been used to optimize renewable energy integration into the grid.
17. Wang, C., et al. (2020). "The Role of AI in Decentralized Energy Systems: A Future Vision."
    Energy Reports, 6, 276-285.
    [DOI: 10.1016/j.egyr.2020.01.010]
    Summary: The paper explores the future role of AI in decentralized energy systems, including microgrids, peer-to-peer energy trading, and community-based renewable energy projects.
18. Zhou, Y., et al. (2021). "Artificial Intelligence and Machine Learning for Renewable Energy: Applications, Challenges, and Future Perspectives."
    Energy, 236, 121348.
    [DOI: 10.1016/j.energy.2021.121348]
    Summary: This review discusses emerging AI techniques for renewable energy and future trends, such as explainable AI and AI-powered autonomous energy systems.