Modelado y gemelos digitales en el contexto fotovoltaico

Fabricio Orlando Sanchez Varretti; Franco Bertani Matung; Juan Cruz Esquembre Santamar\'ia; Ricardo R. Palma

REVIEW 3 major objections 4 minor 28 references

Modelado y gemelos digitales en el contexto fotovoltaico

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that among photovoltaic simulators, SAM is the realistic base for a solar digital twin, while PVsyst operates at best as a digital shadow.

desk verdict A competent practical comparison of PVsyst and SAM, but the claim that SAM is the better digital-twin base is a plausible opinion backed by a feature checklist, not by a demonstrated closed-loop integration. read the letter →

arxiv 2506.12102 v1 pith:WCHUUZIY submitted 2025-06-13 physics.soc-ph cs.CY

Franco Bertani Matung , Juan Cruz Esquembre Santamaría , Ricardo R. Palma , Fabricio Orlando Sanchez Varretti This is my paper · ORCID

classification physics.soc-phcs.CY

keywords digitaltwinphotovoltaicsystemsPVsystSAMshadowsolarenergysimulationmaturitycasestudy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This report asks whether widely used photovoltaic simulators can serve as digital twins, and on what evidence. It compares PVsyst and SAM on a real rooftop plant in Turkey, using the same NSRDB weather data, and measures simulated against actual monthly energy production. Its central claim is that PVsyst, however accurate as a design tool, operates at best as a digital shadow—it cannot ingest live operating data or feed actions back to the plant—while SAM's scripting and data-access flexibility make it the realistic foundation for a digital twin once external sensors, APIs, and AI modules are added. If the claim holds, the practical takeaway is that simulator choice for operational use should be driven by integration capacity, not by simulation precision alone.

What carries the argument

The argument runs on two coupled devices. First is the three-level maturity taxonomy—digital model (no live data), digital shadow (one-way data inflow), digital twin (bidirectional data and control flow)—which the paper adopts from its references. Second is a feature checklist in Table 5 that assigns checkmarks to criteria such as import of real data, dynamic update, feedback to the physical system, IoT support, machine learning use, scripting/interoperability, and result export; that checklist is what separates PVsyst (shadow) from SAM (between shadow and twin). The quantitative RMSE/MAE comparison in Section 4 supports the accuracy part of the story, while the checklist carries the maturity verdict.

What would settle it

Connect a live data feed and a control channel to both SAM and PVsyst on the same plant; if SAM cannot update its model from streaming sensor data and send recommendations back in real time, or if a PVsyst-based setup with external scripting achieves the same behavior, the paper's central ranking is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is a maturity ranking grounded in the digital-model/digital-shadow/digital-twin taxonomy: PVsyst has high physical fidelity but no native dynamic data loop, so it belongs at the digital-model level or, at best, the digital-shadow level; SAM sits between digital shadow and digital twin, with Python/Matlab scripting, NSRDB data access, and result export that make it extendable to a true digital twin when paired with monitoring platforms, APIs, and artificial intelligence. The paper also reports that both tools give reasonable annual energy estimates for the Elmah case, with monthly deviations captured by RMSE and MAE, but neither tool in its base configuration is a complete digital twin.

Load-bearing premise

The ranking assumes that the paper's feature checklist—real-data import, scripting, dynamic update, feedback—correctly captures what makes a simulator a digital twin, and that SAM's scripting flexibility will actually translate into a working bidirectional loop once external components are added.

Editorial extensions

If this is right

PVsyst remains the stronger tool for design-stage optimization, where historical weather and detailed shading and loss analysis matter more than live data.
SAM can serve as the predictive engine of a solar digital twin, provided a data-acquisition layer with IoT sensors and AI modules is attached.
Choosing a simulator for operational use should weight integration and scripting ability at least as heavily as simulation accuracy.
A full photovoltaic digital twin is not available in either commercial tool out of the box; it has to be assembled from simulator, monitoring, and feedback components.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's error analysis measures energy-estimation accuracy but never exercises the feedback loop, so the maturity conclusion rests on the qualitative feature checklist rather than on the simulation numbers.
A direct test of the paper's recommendation would be to build a SAM-based twin with live sensor data and compare its nowcast accuracy and response time against a static PVsyst model, measuring how much the feedback loop actually improves operations.
The same maturity checklist could be applied to open-source simulation stacks, which already support one-way data ingestion and scripting, and might therefore outrank commercial tools on the path to a digital twin.
The paper places SAM 'between' discrete maturity levels, which suggests that a graded or continuous maturity metric would be more informative than the three-level label.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First, what this is: a solid engineering report comparing two PV simulation tools on a real plant, with a digital-twin maturity classification. The case study is honest and clear. The authors use NSRDB data for both tools, present monthly and annual comparisons, and explicitly say neither tool alone is a complete DT. The conclusion that SAM is a better base for a future DT is conditional on external sensors and AI modules, so the stress-test note that this is unsupported is a bit too harsh. The paper never claims SAM is a DT; it claims SAM is structurally closer.

That said, the structural-closeness argument rests entirely on a feature checklist. The quantitative results actually favor PVsyst on annual energy accuracy (177,523 kWh vs. 175,515 kWh real, against SAM's 163,559 kWh), and the paper doesn't reconcile why flexibility should outweigh accuracy in a DT context. If the authors want to convince a skeptical engineer, they need at least a minimal demonstration: feed live data into SAM via PySAM, push a set-point back, and show the loop. Without that, the recommendation is an interpretation, not a verified property.

The paper's real value is as a decision-support or teaching document for Spanish-speaking engineers. It's well-structured, limitations are stated, and the references look appropriate. No machine-checked proofs or shipped code, so reproducibility is limited. RMSE/MAE figures lack uncertainty bounds, and the input files aren't given.

My recommendation: if this crosses your desk, don't desk-reject it if the venue publishes applied case studies. Send it to review with a request to either add a proof-of-concept integration or soften the conclusion to a clearly labeled 'potential' claim. For a research journal, novelty is too low; for a practice-oriented journal, it's a solid submission.

Referee Report

3 major / 4 minor

Summary. The paper reviews the digital-twin concept for photovoltaic systems, adopts the Digital Model/Digital Shadow/Digital Twin (DM/DS/DT) taxonomy, and applies it to two widely used simulators, PVsyst and SAM. Using a reference rooftop installation in Elmali, Turkey, the authors replicate the system in both tools with a common NSRDB weather input, compare monthly simulated energy with reported real data in Table 6, and compute RMSE/MAE-type errors. On the basis of a feature checklist (Tables 5 and 7), the paper concludes that PVsyst operates at best as a Digital Shadow, while SAM has a more flexible architecture that can be extended toward a Digital Twin when combined with monitoring platforms, APIs, and artificial-intelligence modules (Section 8).

Significance. If the central qualitative claim is accepted, the paper provides a useful, practical screening of two commercial simulation tools for digital-twin projects and a concrete case-study benchmark. Its strengths include a real installation with comparatively detailed panel and inverter parameter tables, the use of the same NSRDB weather data in both simulators, a transparent DM/DS/DT checklist, and an explicit admission that neither tool is, by itself, a complete digital twin. The contribution is primarily an expert-assessment/maturity-classification exercise; the quantitative energy comparison is ancillary to the digital-twin conclusion. The result is plausible but currently rests on a small number of subjective checklist entries rather than on a demonstrated closed-loop workflow.

major comments (3)

[§5, Table 7; §8] The central conclusion that SAM has greater structural maturity for a Digital Twin is based on the entries 'Actualización dinámica con datos reales: Parcial (vía scripting)' and 'Soporte para integración IoT: Parcial (API externa)' in Table 7, but no evidence or reference is provided showing that SAM's Python/MATLAB scripting can ingest live operational data and update the model accordingly, and Table 7 itself marks 'Retroalimentación al sistema físico' as absent for both tools. As written, the conclusion in Section 8 goes beyond the documented capabilities. The authors should either soften the claim to 'SAM is a more promising candidate for future integration, provided an external control layer is built,' or demonstrate a minimal closed-loop example, such as using PySAM to read a time-series file and re-optimize a parameter, and compare that with what PVsyst can or cannot do.
[§3.4, Tables 5 and 7] The maturity rubric is not operationalized. Table 5 uses binary checkmarks for DM/DS/DT, but Table 7 introduces unquantified gradations such as 'Parcial (vía scripting)', 'Parcial (API externa)', 'Limitada', and 'Medio–Alto' without defining thresholds, weights, or an aggregation rule. Consequently, the central ranking of SAM as closer to DT than PVsyst is not reproducible from the stated criteria. The authors should define a scoring protocol, specify what 'Parcial' requires (e.g., a demonstrable data ingestion path, but no automatic feedback), justify each Table 7 entry with a reference or a test, and explain how the entries map onto the DM/DS/DT levels in Table 5.
[§4 and §8] Section 8 states that 'El análisis comparativo también evidenció ... limitaciones importantes en su capacidad de adaptación dinámica,' but the comparisons in Section 4 and Table 6 are static monthly energy estimates; they contain no test of dynamic updating, real-time data ingestion, or bidirectional feedback. The digital-maturity ranking therefore derives entirely from the feature checklist in Section 3.4/Table 7, not from the simulation results, and the current wording conflates the two evidence bases. The paper should state this explicitly and either remove the implication that the quantitative study supports the maturity assessment or add a genuinely dynamic experiment.

minor comments (4)

[§4, Table 6] The accuracy comparison should include units in the table header, a description of how the 'Real' energy values were obtained (metering, inverter logs, or the source paper [15]), and an estimate of measurement uncertainty. The monthly residuals in Table 6 show substantial seasonal bias (e.g., SAM under-predicts January by about 25% and over-predicts June by about 22%), which is not discussed; an analysis of this bias and its possible causes (degradation, soiling, inverter clipping, weather-data mismatch) would strengthen the claim that both simulators give 'razonable' annual estimates.
[Throughout] There are numerous typographical and formatting issues: the byline contains 'YGEMELOSDIGITALES' as a single word, 'Pv-syst' and 'PVsyst' are used inconsistently, Table 4 has 'MPTT' instead of 'MPPT' and 'V oltaje' instead of 'Voltaje', Section 6 bullets begin with '.)', and 'imágen' appears instead of 'imagen'. A careful proofread is needed.
[§3.3 and §4 figures] Several figures (Figures 5–10, 11–17) are not explicitly referenced in the text with the expected 'Figura N' callouts; add in-text references so the reader can connect the screenshots and error plots to the corresponding simulation steps and metrics.
[References] References [18] and [19] appear to be web documentation rather than peer-reviewed sources; if used to support the claim that SAM supports scripting and MATLAB linkage, cite the official PySAM documentation with version and access date. Reference [15] is the likely source of the real plant data, but this should be stated explicitly in Section 3.2 rather than only appearing in the bibliography.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's simulator maturity classification and energy comparisons are based on external benchmarks and a documented feature checklist, not on fitted parameters or self-citation chains.

full rationale

The paper makes no formal derivation that reduces to its own inputs. The central claim, that SAM is structurally closer to a Digital Twin than PVsyst, is supported by a feature checklist (Tables 5 and 7) whose criteria come from the cited DM/DS/DT taxonomy in the literature, not from the paper's own definitions. The quantitative section compares simulated energy values against real operating data from an external case study (Table 6); these are independent benchmarks and not fitted outputs. No parameter is fitted to a subset of data and then renamed as a prediction. The paper explicitly acknowledges that neither simulator reaches full DT status and that SAM's DT potential requires external sensors, APIs, and AI modules, which weakens the conclusion but does not make it circular. Self-citations are not load-bearing because the relevant references (PySAM documentation, PVsyst literature, digital twin reviews) are external technical sources. The classification is a qualitative judgment about feature availability, and the paper's own Table 7 marks physical feedback as absent for both tools. That limitation is explicitly stated, and the conclusion is presented as an extension path rather than as a demonstrated bidirectional feedback capability. Overall, the derivation chain is self-contained against external data and external classification criteria, so no circular step can be identified.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper adds no fitted parameters and invents no new entities. Its conclusions rest on the DM/DS/DT taxonomy, the accuracy of the real plant data, and the consistency of the NSRDB weather data across the two simulators.

assumptions (4)

domain assumption The DM/DS/DT maturity taxonomy is a valid and complete way to classify photovoltaic simulators.
Section 2.1 introduces the taxonomy and Section 3.4 uses it as the evaluation framework; the paper does not justify why this taxonomy is the correct one.
domain assumption The NSRDB weather data is consistent when imported from SAM into PVsyst, so the simulation comparison is fair.
Section 3.2 states that NSRDB data was used in SAM and imported to PVsyst; consistency of the import is assumed.
domain assumption The real production data from the Elmah plant are accurate and representative.
Section 3.2 describes the plant and references [15]; the paper does not discuss measurement uncertainty.
standard math The built-in physical models of PVsyst and SAM are reliable for estimating monthly energy yield.
Section 3.3 uses these models to generate the energy estimates that are compared with real data.

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Cite this review

Pith. "Pith review of Modelado y gemelos digitales en el contexto fotovoltaico." pith.science (2026). https://pith.science/paper/WCHUUZIY

@misc{pith2026250612102,
  author       = {Pith},
  title        = {Pith review of: Modelado y gemelos digitales en el contexto fotovoltaico},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WCHUUZIY}},
  note         = {Machine review of arXiv:2506.12102}
}

read the original abstract

The photovoltaic industry faces the challenge of optimizing the performance and management of its systems in an increasingly digitalized environment. In this context, digital twins offer an innovative solution: virtual models that replicate in real time the behavior of solar installations. This technology makes it possible to anticipate failures, improve operational efficiency and facilitate data-driven decision-making. This report analyzes its application in the photovoltaic sector, highlighting its benefits and transformative potential.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Metodología 3.1. Enfoque general El enfoque general de este trabajo se basa en una metodología de análisis comparativo y exploratorio, cuyo objetivo principal es evaluar el grado de madurez digital de dos herramientas de simulación fotovoltaica: PVsyst y SAM. Esta metodología se estructura en cuatro etapas. En primer lugar, se realiza una caracterización ...

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Tabla 6: Comparación mensual de energía real y simulada con SAM y PVsyst (en unidades de energía)

Resultados A continuación, se presentan los gráficos resultantes de las simulaciones, los cuales muestran la comparación entre los datos estimados y los reales mediante el análisis de la desviación cuadrática media (RMSE) y la desviación absoluta media (MAE) para cada mes del año. Tabla 6: Comparación mensual de energía real y simulada con SAM y PVsyst (e...

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Análisis de capacidades digitales A partir del marco de clasificación DM/DS/DT y los criterios definidos en la metodología, se evaluó el grado de madurez digital de cada simulador. Cómo se puede observar en la imágen, PVsyst opera, en el mejor de los casos, como un Digital Shadow: es capaz de importar condiciones climáticas históricas y modelar con gran d...

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En un entorno donde la producción energética depende de múltiples variables cambiantes, los simuladores estáticos no son suficientes

Discusión técnica La comparación revela que la elección del simulador no solo debe basarse en su precisión técnica, sino en su capacidad de integración en una arquitectura digital más amplia. En un entorno donde la producción energética depende de múltiples variables cambiantes, los simuladores estáticos no son suficientes. .) PVsyst resulta ideal para la...

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Implicancias para la implementación de gemelos digitales Los resultados permiten afirmar que ninguno de los simuladores analizados constituye por sí solo un gemelo digital completo, pero que SAM tiene mayor madurez estructural para integrarse en una arquitectura de tipo DT[ 12]. Esto implica que: .)Puede usarse como motor predictivo dentro de un gemelo di...

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Conclusiones El desarrollo de este trabajo permitió evaluar de manera crítica el grado de madurez digital de dos de los simuladores fotovoltaicos más utilizados actualmente —PVsyst y SAM— a través de su aplicación a un caso de estudio real. A partir de la taxonomía conceptual de modelos digitales propuesta por Grieves y extendida por Jones et al., se conc...

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

renovación

Introducción En los últimos años se ha visto en la Argentina una necesidad creciente de renovar la matriz energética. Impulsado principalmente por una demanda de energía en constante crecimiento y la toma de conciencia de técnicas y procesos más sustentables en la industria. Esto ha dado lugar a un interés en constante aumento de las energías renovables c...

work page 2022

[2] [2]

gemelos digitales

Marco Teórico 2.1. Historia del concepto de gemelo digital El término gemelo digital (digital twin) fue introducido por Michael Grieves en 2002 en el contexto de la gestión del ciclo de vida del producto (Product Lifecycle Management, PLM), y desde entonces ha evolucionado hasta convertirse en una de las tecnologías emergentes más prometedoras para el mon...

work page 2002

[3] [3]

Metodología 3.1. Enfoque general El enfoque general de este trabajo se basa en una metodología de análisis comparativo y exploratorio, cuyo objetivo principal es evaluar el grado de madurez digital de dos herramientas de simulación fotovoltaica: PVsyst y SAM. Esta metodología se estructura en cuatro etapas. En primer lugar, se realiza una caracterización ...

work page

[4] [4]

Tabla 6: Comparación mensual de energía real y simulada con SAM y PVsyst (en unidades de energía)

Resultados A continuación, se presentan los gráficos resultantes de las simulaciones, los cuales muestran la comparación entre los datos estimados y los reales mediante el análisis de la desviación cuadrática media (RMSE) y la desviación absoluta media (MAE) para cada mes del año. Tabla 6: Comparación mensual de energía real y simulada con SAM y PVsyst (e...

work page

[5] [5]

Análisis de capacidades digitales A partir del marco de clasificación DM/DS/DT y los criterios definidos en la metodología, se evaluó el grado de madurez digital de cada simulador. Cómo se puede observar en la imágen, PVsyst opera, en el mejor de los casos, como un Digital Shadow: es capaz de importar condiciones climáticas históricas y modelar con gran d...

work page

[6] [6]

En un entorno donde la producción energética depende de múltiples variables cambiantes, los simuladores estáticos no son suficientes

Discusión técnica La comparación revela que la elección del simulador no solo debe basarse en su precisión técnica, sino en su capacidad de integración en una arquitectura digital más amplia. En un entorno donde la producción energética depende de múltiples variables cambiantes, los simuladores estáticos no son suficientes. .) PVsyst resulta ideal para la...

work page 2023

[7] [7]

Esto implica que: .)Puede usarse como motor predictivo dentro de un gemelo digital solar

Implicancias para la implementación de gemelos digitales Los resultados permiten afirmar que ninguno de los simuladores analizados constituye por sí solo un gemelo digital completo, pero que SAM tiene mayor madurez estructural para integrarse en una arquitectura de tipo DT[ 12]. Esto implica que: .)Puede usarse como motor predictivo dentro de un gemelo di...

work page

[8] [8]

Conclusiones El desarrollo de este trabajo permitió evaluar de manera crítica el grado de madurez digital de dos de los simuladores fotovoltaicos más utilizados actualmente —PVsyst y SAM— a través de su aplicación a un caso de estudio real. A partir de la taxonomía conceptual de modelos digitales propuesta por Grieves y extendida por Jones et al., se conc...

work page