Enhanced Solar Water Pumping Using Bifacial PV Modules with Reflective Augmentation and AI Driven Yield Prediction

This research presents a hybrid solar water pumping system that integrates bifacial photovoltaic (PV) modules with static planar reflectors to enhance energy capture in rural and off-grid environments. The system improves rear-side irradiance through passive reflective augmentation, thereby increasing total energy yield without relying on mechanical tracking mechanisms. Optical simulations conducted using TracePro confirmed substantial gains in rear-side irradiance, while field experiments demonstrated up to a 31% increase in daily water output and over 12% improvement in pump efficiency compared to conventional fixed-tilt monofacial PV systems. To support system design and operational planning, two artificial intelligence models were employed: a feedforward Artificial Neural Network (ANN) for short-term performance estimation, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for monthly and annual energy yield prediction. The ANFIS model forecasts an approximate 31% annual energy gain for the bifacial-reflector configuration over traditional mono facial setups. This integrated approach offers a robust, low-maintenance, and scalable solution for providing sustainable water access in high-albedo, infrastructure-limited regions.