Journal of Alternate Energy Sources & Technologies Review Article
AI-Driven Optimisation of Renewable Energy Systems: A Review of Smart Grid Integration, Lightweight Models, and IoT-Enabled Energy Management
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
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