Sky Image Classification Based Solar Power Prediction Using CNN
Bibliographic record
Abstract
Fossil fuels are diminishing at an alarming rate in today's generation.The usage of renewable energy sources has emerged as the plan most likely to succeed in the long term.As a result, renewable energy supplies are being encouraged owing to their eco-friendliness, inexhaustibility, cheap cost, dependability, and resilience.The amount of the negative impact on the functioning of electrical networks caused by this change is proportional to the capacity of the particular station.Accurate forecasting of solar photovoltaic power on a minute-by-minute basis is beneficial to the functioning of the energy market, consumption of solar photovoltaic power, and power system stability.In this study, a CNN classifier (AlexNet) is proposed to classify the images of sun.The PSO based segmentation method is used to defog the sun image.The performance of the proposed method is evaluated for different architecture of CNN.For this we comparing proposed AlexNet to the other two models (VGGNet, GoogLeNet) to know the accuracy of our proposed model.To evaluate the accuracy performance metrics is used.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".