Modelling, Simulation, Optimization, And Machine Learning for Photovoltaic System Performance: A Comprehensive Review with Perspectives for High-Irradiance Developing Regions

designs, evaluation conventions, and training materials would let advances at any scale propagate to the others, and would concentrate scarce regional engineering capacity on adaptation rather than reinvention. 12. Conclusion This review has synthesized the literature across the engineering decision chain that determines photovoltaic system performance: device and array modelling, simulation and validation, operating point control and power electronic integration, orientation and tracking, metaheuristic optimization with techno-economic assessment, and the machine learning layer now being drawn over all of them. The classical streams are mature: diode models and their extraction methods are standardized, simulation environments are validated and increasingly open, maximum power point tracking is consolidated, tracking and tilt optimization are well characterized, and evolutionary optimization coupled with levelized cost analysis defines credible design practice. The learning stream is the active frontier, and its strongest work is distinguished by chronological evaluation discipline, physical plausibility auditing, and deployment architectures that keep human judgment engaged. For high-irradiance developing regions, the synthesis carries a specific message. The binding constraint on efficient photovoltaic deployment is no longer method availability or tool cost; it is regionally validated application. The highest-value research investments are measurement campaigns, locally calibrated benchmarks, and reproducible pipelines built on open tooling, because these convert the global methodological inheritance into locally trustworthy engineering practice. The highest-value deployment investments are design-stage modelling and optimization, because the capital-dominated economics of the technology concentrate lifetime performance in decisions made before installation, and operational intelligence that protects that performance over the asset life. The trajectory across all streams points the same way: predictive intelligence migrating from hardware to software, from generic assumption to local data, and from component optimization to lifecycle and system framing. Research and practice that align with this trajectory, and that carry its benefits deliberately into the regions where energy access needs are greatest, will define the next decade of photovoltaic performance engineering. Declarations