Investigation of PV System Tilt Angle by Neural Network Modelling of Energy Balance

Abstract This study presents a novel multidimensional computational framework for optimising the tilt angle of photovoltaic systems by utilising complex variable modelling, transfer function analysis, and artificial neural network modelling. A 35 kWp photovoltaic system was monitored continuously over one year at a fixed 25° tilt, and comprehensive statistical analyses were conducted to evaluate monthly and seasonal variations in energy output. The proposed methodology makes differential equations with constant coefficients and transfer function models to describe time-dependent energy balance, enabling robust dynamic system identification. Empirical relationships between tilt angle and produced energy yield were effectively quantified, with a negative correlation coefficient value of 0.95 observed between steady-state energy production and tilt angle. Comparative analysis demonstrated negligible differences in annual yield between the 25° and 35° tilt configurations. Validation by neural network, utilising various architectures, revealed that a single hidden layer with 12 neurons achieved optimal model prediction performance value of 99.15%. The results confirm the efficiency of combining complex variable models with AI-based validation for precise and practical PV system modelling. This integrated approach facilitates improved decision-making in PV design by providing accurate predictions of energy yield under varying operational conditions.