Seasonal Clustering Forecasting Technique for Intelligent Hourly Solar Irradiance Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main purpose of the study is to reach a forecasting accuracy level through layering and stacking clusters of weather data to reduce seasonality-related uncertainty. To enhance forecasting accuracy, high-dimensional heterogeneous weather data should be added to training datasets; yet, weather data are deemed to have seasonality-related uncertainty. Since utilizing the long short-term memory (LSTM) model does not achieve the required results, the LSTM forecasting model performance deteriorates as a result of the change from univariate to multivariate analyses. Therefore, a primary investigation was conducted to analyze seasonality-based hourly predictions for global horizontal irradiance. The investigation found that seasonality affects accuracy of predictions due to high levels of Autumn- and Winter-related weather phenomena and climate uncertainty. Accordingly, seasonal clustering forecasting technique (SCFT) based on an LSTM hybrid strategy and stacked layers of weather clusters was proposed. In order to demonstrate the validity of this model, the proposed SCFT is compared to a variety of forecasting approaches in the literature. The comparison shows the SCFT’s superiority, especially during seasons that are predominantly rainy and overcast. Finally, in accordance with previous studies, suggesting the benefit of applying model testing in other parts of the world, SCFT is employed using data from other Köppen climate classifications. Furthermore, the performance of the proposed forecasting model, SCFT, shows significant stability and reliability after measuring it against Diebold–Mariano and Granger–Newbold tests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it