Using Neural Network Decomposition to Estimate Field Photovoltaic Performance Loss Rate
Bibliographic record
Abstract
Estimation of Photovoltaic (PV) Performance Loss Rate (PLR) becomes increasingly important in the stage of rapid growth of PV industry. Accurate PLR estimation benefits PV users by providing real-time monitoring of the PV modules' performance. Explainable PLR estimation can help PV manufacturers study and improve performance of their products. However, traditional PLR estimations, based on statistical models, have some major disadvantages. First, they need user knowledge and decisions. Second, they tend to be less robust to non-uniform and low-quality data. To address these issues, we propose the NN-PLR, a Neural Network decomposition method for field PV PLR estimations. NN-PLR decomposes the power timeseries data into seasonality, trend, and remainder components and uses trend to estimate PLR. Decompositions used to derive PLR can reveal how PLR vary and change across different PV systems. Using digital power plant datasets, we have demonstrated that NN-PLR can produce comparable PLR estimation results to traditional PLR estimation methods while needing much less input tweaks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".