IMPLEMENTATION OF THE DEVELOPED SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE MODEL IN REAL-WORLD APPLICATIONS FOR SUSTAINABLE ENERGY OPTIMIZATION

This study presents an implementation-based investigation into the deployment and real-world validation of a developed solar photovoltaic (PV) system performance model designed to support sustainable energy optimization. Unlike prior works that primarily focused on theoretical formulations or simulation-driven analyses, this research emphasizes the direct application and operational evaluation of the model under diverse environmental and infrastructural conditions. The implementation was carried out across multiple grid-connected PV installations varying in scale, configuration, and climatic exposure to capture a broad spectrum of operational challenges. The framework involved integrating real-time data acquisition systems to continuously monitor solar irradiance, module temperature, ambient conditions, electrical output, and system losses, which were then processed through the developed model for real-time performance estimation. Empirical validation involved comparing model-predicted energy yields with measured field data over a 12-month operational period, incorporating seasonal variability, shading impacts, and maintenance-induced downtimes. The results showed that the implemented model achieved high predictive accuracy, with mean absolute percentage error consistently below 5%, while also identifying site-specific inefficiencies such as temperature-induced derating, mismatch losses, and inverter clipping. The implementation further demonstrated that coupling model predictions with adaptive control strategies enhanced energy harvesting efficiency, reduced curtailment, and improved system reliability. In addition to performance validation, the study ensured compliance with international standards such as IEC 61724 for performance monitoring and IEC 61853 for module characterization, enhancing the replicability and interoperability of the model in heterogeneous energy environments. This practical deployment bridges the gap between conceptual model design and field-based energy management by translating complex algorithms into operational decision-support tools. The findings affirm the model’s readiness for large-scale adoption, offering a scalable and adaptive framework to optimize PV system performance, improve return on investment, and accelerate the transition to sustainable energy systems in real-world contexts.