Optimizing Solar Water-Pumping Systems Using PID-Jellyfish Controller with ANN Integration

This study presents a novel approach to improving the efficiency and reliability of solar water pumping systems by integrating a proportional–integral–derivative (PID) controller with the Jellyfish Algorithm (PID-JC) and artificial neural networks (ANN). Solar water-pumping systems are gaining attention due to their sustainable and eco-friendly nature; however, their performance is often limited by fluctuating solar irradiance and varying water demand. To address these challenges, Monte Carlo simulations were employed to account for system uncertainties. Traditional PID controllers, although widely used, often struggle to adapt effectively to dynamic environmental conditions. The proposed system utilizes an ANN to predict solar irradiance and water demand patterns based on historical data, enabling real-time adjustments of pump operations through the PID-JC. This approach is inspired by the adaptive behavior of jellyfish in dynamic environments. The PID-JC adjusts PID parameters dynamically based on ANN predictions, optimizing pump performance. Simulation and experimental results conducted on a solar water-pumping system in Mrada City, Northeastern Libya, demonstrated significant improvements in water delivery, energy consumption, and system reliability compared to conventional PID controllers. The PID-JC’s ability to adapt to diverse environmental conditions ensures robust performance across various geographical locations and seasonal changes. Additionally, comparisons to other optimization algorithms, such as Firefly and Golden Eagle Optimization, show that the Jellyfish Algorithm outperforms them with a 6.30% improvement in the cost function and a 28.13% reduction in processing time compared to Firefly, and a 26.81% improvement in the cost function and a 20.69% reduction in processing time compared to Golden Eagle Optimization.