GIS-based optimization of photovoltaic panel orientation in Benin using genetic algorithms and K-means clustering

• Published in: Solar Compass Vol. 16; p. 100142
• Main Authors: Télesphore, Nounangnonhou Cossi, Richy, Aza-Gnandji Maurel, Fiacre, Zantou, Melhyas, Kple, Audace, Didavi Kossoko Babatoundé, Pierre, Aguemon Dourodjayé, Isaac, Amoussou, Clarence, Semassou Guy, François-Xavier, Fifatin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025; Elsevier
• Subjects: Azimuth angle; Benin; Genetic Algorithm; K-means clustering; Kneedle algorithm; Perrin-de-Brichambaut model; Tilt angle; Tilt angle; Benin; Azimuth angle; Kneedle algorithm; Genetic Algorithm; Perrin-de-Brichambaut model; K-means clustering
• ISSN: 2772-9400; 2772-9400
• DOI: 10.1016/j.solcom.2025.100142

•A national-scale atlas of optimal PV panel orientation is developed for Benin.•Tilt and azimuth angles are optimized using Genetic Algorithms and 2023 NASA data.•K-means clustering yields 8 standard PV mounting kits with >95 % yield retention.•The workflow balances energy yield, design simplicity, and spatial resolution. Accurately determining the orientation of fixed-tilt photovoltaic (PV) panels is critical for maximizing annual energy output, yet most installations in Benin continue to rely on empirical or generalized guidelines. This study develops a high-resolution (0.05° × 0.05°) geospatial database of optimal tilt (β*) and azimuth (α*) angles across the country by coupling the Perrin-de-Brichambaut clear-sky model with a Genetic Algorithm, that uses 2023 NASA-POWER meteorological inputs. Results show that β* increases from approximately 6° along the southern coast to around 13° in the far north, while α* evolves from a slight westerly orientation to a moderate easterly preference above 8°N latitude. To facilitate practical implementation, K-means clustering was applied separately to both angle datasets, with the number of clusters determined via the Kneedle algorithm. The resulting 3 tilt and 3 azimuth clusters combine into 8 implementation-ready mounting configurations that retain >95 % of the energy yield provided by fully site-specific optimization, while substantially simplifying system design. The accompanying orientation atlas, GIS layers, and open-source Python code offer a scalable and evidence-based planning tool for PV deployment in Benin, supporting policymakers, engineers, and developers in accelerating the energy transition. [Display omitted]