DEVELOPMENT OF A PREDICTIVE SIMULATION MODEL FOR SOLAR PHOTOVOLTAIC SYSTEM PERFORMANCE ANALYSIS CONSIDERING ENVIRONMENTAL, TECHNICAL, AND ECONOMIC EFFICIENCY FACTORS

Abstract

This study presents the development of a comprehensive predictive simulation model for solar photovoltaic (PV) system performance analysis by integrating environmental, technical, and economic efficiency factors into a unified framework. Recognizing that existing models often assess these domains in isolation, this research aimed to construct a holistic and modular approach capable of capturing the full causal chain from climatic variability through technical energy conversion to long-term financial viability. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure methodological rigor, transparency, and reproducibility. In total, 524 peer-reviewed articles published across the past two decades were examined, encompassing 142 environmental modeling studies, 167 technical performance studies, 123 techno-economic studies, 64 integration-focused studies, and 58 uncertainty and sensitivity analysis studies, representing a combined citation volume exceeding 50,000 scholarly references.The synthesis revealed that accurate environmental resource modeling—particularly solar irradiance transposition, thermal behavior estimation, and stochastic soiling dynamics—forms the foundational determinant of yield prediction accuracy. Detailed technical modeling of modules, inverters, balance-of-system losses, and geometric layout optimization emerged as critical for converting environmental inputs into realistic DC and AC power outputs. Economic modeling findings emphasized that metrics such as levelized cost of electricity, net present value, and internal rate of return are highly sensitive to performance deviations, underscoring the need to embed financial modules directly within performance simulations. The study also found that fully integrated models, which simultaneously link environmental, technical, and economic layers while embedding end-to-end uncertainty propagation and sensitivity assessment, reduced prediction error from approximately ±12% to ±5% and improved investment decision reliability. Overall, this study contributes a robust conceptual and methodological foundation for developing predictive PV simulation models that are technically precise, economically credible, and transferable across diverse climatic and market contexts.