AI-Driven Optimisation of Renewable Energy Systems: A Review of Smart Grid Integration, Lightweight Models, and IoT-Enabled Energy Management

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Year : 2026 | Volume : 17 | 03 | Page :
By

Sumit Pratap Singh,

Vansh Sahani,

Aneesh Singh,

Shivansh Tiwari,

Vineet Kumar Tiwari,

Satrughan Mishra,

  1. UG Student, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India
  2. UG Student, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India
  3. UG Student, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India
  4. UG Student, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India
  5. Associate Professor, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India
  6. Assistant Professor, Department of Electrical Engineering, United College of Engineering & Research, Prayagraj, Uttar Pradesh, India

Abstract

The energy sector is undergoing a transformation with the help of artificial intelligence (AI), which enhances the efficiency, reliability, and sustainability of renewable energy systems. The world is increasingly moving towards a low-carbon economy and away from fossil fuels, and AI-powered tools are becoming increasingly popular. The paper highlights recent strides in the field of AI for sustainable energy, emphasizing lightweight AI models, systems with Internet of Things (IoT) integration, and data-driven analytics to enhance the efficiency of renewable energy sources and reduce environmental impact. The light weightness of AI models provides a reduced computational burden, lower operating energy consumption and suitable real-time decision making in large-scale energy systems. These models enable smart grid applications such as real-time monitoring, predictive maintenance, load balancing and automatic fault detection. The addition of IoT sensor integration adds another layer of energy intelligence with a continuous stream of data from the environment and operations. With the integration of blockchain, IoT- based platforms enhance transparency, cybersecurity, and trust in distributed energy markets. In smart cities, AI-driven reinforcement learning and optimization methods are instrumental in enhancing energy planning and optimization, ensuring the seamless integration of renewable energy sources such as solar and wind power and boosting overall cost-effectiveness. Data quality requirements, cybersecurity risks, and the energy efficiency of powerful AI models pose significant challenges, necessitating responsible innovation, better governance, and sustainable computing practices. The review confirms that AI is a key enabler to help speed up the global shift towards sustainable, low-carbon and resilient energy systems.

Keywords: AI, energy integration, smart grid, internet of things, machine learning, lightweight AI models, reinforcement learning, energy management system, sustainable engineering, green technology

How to cite this article: Sumit Pratap Singh, Vansh Sahani, Aneesh Singh, Shivansh Tiwari, Vineet Kumar Tiwari, Satrughan Mishra. AI-Driven Optimisation of Renewable Energy Systems: A Review of Smart Grid Integration, Lightweight Models, and IoT-Enabled Energy Management. Journal of Alternate Energy Sources & Technologies. 2026; 17(03):-.
How to cite this URL: Sumit Pratap Singh, Vansh Sahani, Aneesh Singh, Shivansh Tiwari, Vineet Kumar Tiwari, Satrughan Mishra. AI-Driven Optimisation of Renewable Energy Systems: A Review of Smart Grid Integration, Lightweight Models, and IoT-Enabled Energy Management. Journal of Alternate Energy Sources & Technologies. 2026; 17(03):-. Available from: https://journals.stmjournals.com/joaest/article=2026/view=261897

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Ahead of Print Subscription Review Article
Volume 17
03
Received 06/10/2026
Accepted 28/10/2026
Published 29/10/2026
Publication Time 23 Days

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