{"id":3180,"date":"2026-01-26T17:48:39","date_gmt":"2026-01-26T17:48:39","guid":{"rendered":"https:\/\/lucisun.com\/?page_id=3180"},"modified":"2026-01-26T18:26:15","modified_gmt":"2026-01-26T18:26:15","slug":"modelling-approach","status":"publish","type":"page","link":"https:\/\/lucisun.com\/es\/soluciones\/lusim\/modelling-approach\/","title":{"rendered":"Modelizaci\u00f3n"},"content":{"rendered":"
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Soluciones\/Lusim<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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El rendimiento de un sistema fotovoltaico rara vez se rige por un \u00fanico par\u00e1metro de dise\u00f1o. En muchos proyectos, la producci\u00f3n de energ\u00eda, las p\u00e9rdidas y el rendimiento econ\u00f3mico dependen de combinaciones de variables que interact\u00faan, como la geometr\u00eda, la separaci\u00f3n, la altura, la orientaci\u00f3n, las propiedades del suelo, la estrategia de seguimiento, la capacidad de almacenamiento y el entorno.<\/p>

In complex PV systems, these interactions cannot be captured reliably through isolated sensitivity checks or single deterministic simulations. Multiparameter optimisation is therefore required to explore design spaces systematically and to evaluate how technical choices translate into both energy and financial outcomes.<\/p>

LuSim se ha desarrollado para apoyar este tipo de an\u00e1lisis dentro de un marco coherente de modelizaci\u00f3n en 3D que vincula la modelizaci\u00f3n f\u00edsica con los indicadores de rendimiento econ\u00f3mico.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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GPU-based 3d simulation framework<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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LuSim relies on a GPU-based 3D simulation framework to represent photovoltaic systems and their surroundings explicitly in space. Terrain, support structures, modules, and nearby objects are modelled directly in three dimensions.<\/p>

The GPU is used to efficiently evaluate geometric visibility and interactions between surfaces. This enables the computation of direct, diffuse, and reflected irradiance components with high spatial resolution, while keeping computation times compatible with parametric studies and batch simulations.<\/p>

This approach allows LuSim to handle complex geometries without reducing them to simplified two dimensional assumptions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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View factor based irradiance modelling<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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At the core of LuSim lies a view factor based formulation of irradiance exchange between surfaces. Each surface element interacts with the sky, the ground, and surrounding objects according to their relative orientation, visibility, and optical properties.<\/p>

This formulation is applied consistently to direct, diffuse, and reflected components. Irradiance can be evaluated locally on photovoltaic modules, ground surfaces, crops, or other targets, rather than being limited to aggregated values.<\/p>

By working with spatially resolved quantities, LuSim makes it possible to analyse heterogeneity effects that are often averaged out in simplified models.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Representation of materials and surfaces<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Surfaces in LuSim are associated with material properties that govern their interaction with incoming radiation. These properties influence reflected irradiance, bifacial gains, and light redistribution within the scene.<\/p>

Rather than relying on uniform assumptions or fixed correction factors, LuSim allows materials to be represented explicitly in space. This makes it possible to study the impact of partial coverage, local treatments, or heterogeneous environments.<\/p>

The level of material detail can be adapted to the objectives of the study, ensuring consistency between modelling effort and expected accuracy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Spatial and temporal resolution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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LuSim evaluates irradiance with explicit spatial and temporal resolution. Targets can be defined at different scales, from individual module regions to aggregated zones representing larger system components.<\/p>

This enables the analysis of time dependent effects such as shading evolution, tracking behaviour, or seasonal variations, as well as spatial patterns that influence performance, degradation, and uncertainty.<\/p>

Resolution choices are made to balance physical relevance and computational efficiency, depending on the purpose of the analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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From irradiance to PV energy yield<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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LuSim implements a complete photovoltaic energy yield modelling chain, from irradiance evaluation to electrical energy production and system losses. The modelling approach follows established engineering practice and relies on scientifically validated models at each step of the simulation process.<\/p>

The accurate evaluation of irradiance in complex 3D environments provides the physical basis of the simulation. This irradiance is converted into electrical power and energy yield using state of the art photovoltaic performance models that account for module behaviour, temperature effects, and system level losses.<\/p>

LuSim integrates a combination of open source libraries and models described in the scientific literature. Open source tools such as pvlib are used where appropriate to ensure transparency and alignment with community validated practices. When relevant models are described in the literature but not available as ready to use open source implementations, they are implemented directly in LuSim based on their published formulations.<\/p>

By relying on peer reviewed models rather than proprietary formulations, LuSim ensures consistency with current scientific knowledge while preserving flexibility in model selection. Each step of the PV energy yield simulation is therefore defined by scientific validity and relevance to the problem being addressed.<\/p>

By combining advanced 3D irradiance modelling with validated electrical and loss models, LuSim supports full PV energy yield assessments suitable for engineering studies and decision support, including cases where geometric and environmental complexity plays a dominant role.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Scope, assumptions, and limitations<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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No modelling approach is universally optimal. LuSim has been designed for cases where geometry driven effects and spatial variability cannot be neglected.<\/p>

Its scope and assumptions are explicitly defined, which allows users to understand where the approach provides added value and where simpler methods may be sufficient. This clarity is essential for interpreting results correctly and for integrating them into broader engineering and financial workflows.<\/p>

By making limitations explicit rather than implicit, LuSim supports informed modelling choices rather than black box usage.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Relationship to other modelling approaches<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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LuSim does not aim to replace all existing photovoltaic simulation methods. It complements them by addressing cases where simplified geometrical assumptions are no longer adequate.<\/p>

Compared to ray tracing approaches, the GPU-based view factor methodology adopted in LuSim offers a different balance between physical detail and computational cost. This makes it well suited for systematic design exploration, multiparameter analysis, and uncertainty studies in complex environments.<\/p>

The choice of modelling approach should always be guided by the nature of the system and the questions being addressed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Soluciones\/Lusim El rendimiento de los sistemas fotovoltaicos rara vez est\u00e1 determinado por un \u00fanico par\u00e1metro de dise\u00f1o. En muchos proyectos, el rendimiento energ\u00e9tico, las p\u00e9rdidas y el rendimiento econ\u00f3mico dependen de combinaciones de variables interconectadas como la geometr\u00eda, el espaciamiento, la altura, la orientaci\u00f3n, las propiedades del terreno, la estrategia de seguimiento, la capacidad de almacenamiento y el entorno circundante. En sistemas fotovoltaicos complejos, estas interacciones no se pueden evaluar de manera fiable mediante an\u00e1lisis de sensibilidad aislados [\u2026]<\/p>","protected":false},"author":2,"featured_media":0,"parent":1939,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_acf_changed":false,"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-3180","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nModelling approach - Lucisun<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lucisun.com\/es\/soluciones\/lusim\/modelling-approach\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modelling approach - Lucisun\" \/>\n<meta property=\"og:description\" content=\"Solutions\/Lusim Photovoltaic system performance is rarely governed by a single design parameter. 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