Real-Time Solar Panel Efficiency Optimisation Using AI and IoT

Solar energy is of great importance in addressing the energy crisis and climate change facing the world, as it offers a clean, renewable energy source. However, the maximisation of the efficiency of solar panel systems is a serious practice due to fluctuating environmental conditions, poor energy management, and inappropriate responses to repairs. Standard monitors and control systems often face constraints, including high latency, limited scalability, and a lack of real-time capabilities, which, in turn, limit overall system performance. To address these issues, the present paper proposes a new real-time optimisation system that leverages Artificial Intelligence (AI) and the Internet of Things (IoT) to maximise the productivity of solar panels. The proposed model will implement an AI-driven, adaptive edge-computing solution that leverages federated learning across distributed IoT devices on solar farms. Because of such a decentralised and intelligent system, it can dynamically monitor, predict, and optimise, as operating parameters are constantly varied and maintenance activities are expected. The framework enables low latency, high energy efficiency, and high data privacy by processing data locally and training the model at the edge, without transferring raw data to the central server. When using this method, network traffic is reduced, maximum fault tolerance is achieved, and it dynamically adapts to the varying conditions of the environment and the process. The system design is highly scalable and aligns with other emerging paradigms in edge intelligence, distributed machine learning, and energy infrastructure sustainability. Experimentally, the solution demonstrated improves energy production, system responsiveness, and operational reliability. The project will introduce a scalable, cost-effective, and environmentally friendly solution to upgrading next-generation solar power systems with innovative, decentralised technologies.

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