Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology
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
Reducing CO2 emissions is a key goal of the strategy for a low-carbon economy and for the choice of greenhouse gas emission mitigation path. An effective forecasting method can represent a useful tool for managing renewable energies in microgrids and mitigating carbon dioxide emission. In this study is evaluated the trend of CO2 emission in Iran, Canada and Italy and compared the CO2 emission from consumption of energy sources: Coal - Natural Gas - Petroleum and other refined hydrocarbons - Renewable Energies. Furthermore, a proposed intelligent method has been provided for CO2 emission forecasting based on Generalized Regression Neural Network and Grey Wolf Optimization. Furthermore, the proposed method has been used for renewable energies generation (Wind power and Solar power) forecasting in the microgrid of Favignana island (Italy). The obtained results confirm the higher accuracy of the proposed method in long-term CO2 emission forecasting and short-term renewable energies generation as compared with other several methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it