Systematic Analysis and Computational Intelligence Based Modeling of Photovoltaic Power Generation in Snow Conditions
Bibliographic record
Abstract
Power prediction for photovoltaic (PV) installations in northern snow-prone areas remains a challenging problem. The behavior of a partially/fully snow-covered PV panel can be complex depending on the snow-related factors, disregarding, which causes large prediction errors in snow conditions. To address this issue, a systematic analysis of the effects of 16 meteorological parameters on the hourly power prediction for systems in a snow-prone area is conducted in this article. According to the best of our knowledge, such comprehensive analysis on various snow-related conditions datasets, i.e., full, snow-free condition, snow condition, and snow-cover condition datasets, for several PV systems is performed for the first time in the literature. The three latter datasets are extracted from the full dataset using a proposed three-step procedure. Moreover, different computational intelligence techniques are implemented to develop hourly prediction models for each dataset of each system. A detailed comparison is then performed between the performance of the proposed models, the Marion model, a modified version of the Marion model, the classic PV model, and a computational intelligence model combined with the Marion model's snow cover detection method. The hourly values of the electrical and meteorologicalparameters for 17 PV systems across Canada, with an aggregated time period of more than 55 years, have been extracted to perform the study. As the results show, categorizing data using the proposed three-step procedure and developing specific computational intelligence models for each condition can significantly improve the prediction accuracy especially when a full/partial snow cover is probable on the panels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".