DUET: A Novel Energy Yield Model With 3-D Shading for Bifacial Photovoltaic Systems
Bibliographic record
Abstract
Bifacial photovoltaic (PV) performance models strive to accurately quantify rear-incident irradiance. While ray tracing models are optically rigorous, they require significant computational resources; faster view factor (VF) models are widely adopted but require user-defined loss factors to approximate rear shading and irradiance nonuniformity, introducing uncertainty in energy yield predictions. This article describes DUET—a bifacial PV performance model that calculates optical and electrical performance based on a physically representative array geometry. DUET’s novel shading algorithm pairs a 3-D VF model with deterministic ray-object intersections to capture 2-D shade-inclusive irradiance profiles while minimizing computational cost. Series and parallel combination of current–voltage curves capture irradiance nonuniformity throughout the module and array. This article provides validation against open-access system measurements from a test site in Roskilde, Denmark, and comparison to other software tested there [1]. DUET’s modeled bifacial energy yield agrees with measured data within −1.56% for fixed-tilt and −0.65% for horizontal single-axis tracked (HSAT) systems. Mean absolute error (MAE) in hourly bifacial power is 14.2–15.0 mW/Wp for fixed-tilt and 17.3–18.3 mW/Wp for HSAT, depending on the module temperature model applied. Comparing modeled and measured rear irradiance of two rear-facing pyranometers, DUET’s MAE values of 2.8 W/m$^{2}$for fixed-tilt and 3.7 W/m$^{2}$for HSAT are among the lowest errors reported for other software tested at this site. DUET provides computationally efficient bifacial performance modeling with geographic, temporal, and structural specificity to determine loss factors for use in other performance models or to be used directly in system design optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".