AI and IoT-Driven Optimization of Solar Thermal Collectors for Industrial Heat Applications

Solar thermal collectors may be optimized for industrial heat applications in a new way due to the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technology. To improve the efficiency and effectiveness of solar thermal collectors, this research introduces a system that uses IoT sensors in combination with Artificial Neural Networks (ANNs). The ANN model receives real-time data from IoT sensors that measure fluid flow rate, temperature, and sun irradiation. The network analyses this data and makes predictions to optimize the solar thermal collectors' operating characteristics under different climatic and operational situations. The AI-driven system continuously changes the collectors' settings to optimize heat collection and reduce energy losses. Both operating expenses and energy usage are decreased because of this optimization, which increases overall system efficiency. The dataset used for this research is the Solar Thermal Collector Data, accessible on Mendeley, which offers environmental characteristics such as solar radiation, ambient temperature, and heat flow for forecasting the average fluid temperature in solar thermal systems. The experimental findings demonstrate that the ANN-based strategy is successful, outperforming previous approaches by a wide margin. Optimization of solar thermal collectors by AI and IoT-driven artificial neural networks resulted in a 15-25% increase in efficiency, a 90% enhancement in fault prediction accuracy, and a reduction in heat losses.