Study of the benefits in optimizing the placement and configuration of photovoltaic panels.

Shading and low irradiance hinder PV energy production; thus optimizing the design of photovoltaic systems is essential for increasing energy output while reducing losses from electrical mismatches. This thesis proposes a comprehensive simulation-based framework for optimizing panel architecture, rooftop placement tactics, and series grouping procedures, which makes use of irradiance data analysis and algorithmic decision-making. The study’s initial phase evaluates the shading resilience of PV modules under identical irradiance. The outcomes highlighted better performance of shingled and half-cell modules compared to conventional full-cell panels, using in-depth I/V characteristic analysis and long-term energy production models. Starting from these findings, the study presents a data-driven approach to improve panel installation on rooftops. Building on previous research, the approach employs irradiance mapping and clustering to improve panel location. Furthermore, an automatic series grouping technique based on cross-time-series correlation is applied in order to ensure reduced losses due to current limitations, connecting to the same inverter panels which operate under similar irradiance conditions. Several real-world-inspired rooftop scenarios are simulated in order to assess the efficacy of the suggested methodology. The automatic panel placement algorithm and grouping strategy are contrasted with manual (by-eye) choices. The results reveal a production increase over hand placement, particularly in complex shading scenarios. Lastly, a ROI analysis evaluates cost-benefit trade-offs in panel selection, grouping, and inverters. This study emphasizes the relevance of data-driven decision-making in PV system design, by showing that algorithmic methodology provides a safe economic return reducing most mismatch losses. The created framework offers a scalable tool for researchers and industry to optimize solar energy.