Performance evaluation of predictive models for a Grid-Connected Building-Integrated photovoltaic system (BIPV)
Bibliographic record
Abstract
Building-integrated photovoltaics (BIPV) emerge as essential components in sustainable urban infrastructure, yet predictive modeling of their thermal and electrical behaviour remains underexplored. This study evaluates two widely used numerical photovoltaic models, a Simplified and a Detailed model, for their ability to predict the thermal and electrical performance of a BIPV rainscreen system under varying irradiance conditions and temporal resolutions. High-resolution (5-minute), mid-resolution (15-minute), and low-resolution (1-hour) simulations are conducted to reflect applications in research and development (R&D), demand side management (DSM), and energy prediction, respectively. Model outputs are validated against experimental data using six statistical indicators. Results show that the Simplified model performs well across temporal resolutions with a consistent overprediction particularly under low-irradiance conditions with its overall accuracy improving at lower resolutions (1-hour). Conversely, the Detailed model excels in simulations with high temporal resolution (5-min), capturing transient irradiance and temperature effects, but shows reduced accuracy when input data are temporally aggregated. These findings highlight the importance of selecting models based on application-specific requirements. The Detailed model is recommended for research and development, while the Simplified model is more suitable for building performance simulations. The study also emphasizes the need for better thermal modelling and further validation of numerical models across a variety of BIPV configurations and climatic conditions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".