Uncertainty-aware energy forecasting and environmental impact simulation using Monte Carlo and deep learning
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
This paper presents a simulation system utilizing Long Short-Term Memory (LSTM) forecasting models combined with Monte Carlo methods to predict energy generation and model uncertainty from renewable and fossil fuel sources in the Adrar region of Algeria. The study focuses on short-term load forecasting for the Kabertane wind field, the Adrar solar photovoltaic farm, and overall electrical demand. Separate LSTM models predict each renewable source’s output, which are aggregated and subtracted from load demand forecasts to determine necessary fossil fuel requirements. Monte Carlo methods quantify uncertainty by fitting error distributions to forecast residuals and generating multiple realizations with added noise. This probabilistic approach provides robust assessment of economic costs and environmental impacts of energy production. Results demonstrate significant cost savings and CO2 emission reductions through renewable energy incorporation while emphasizing the critical role of uncertainty modeling in optimizing energy production, cost, and environmental sustainability in the region.
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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