L'intelligence artificielle au service de l'optimisation de l'énergie électrique dans un réseau intelligent
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
System operations and planning is a crucial aspect of power system management.It aims to maintain the equilibrium between supply and demand of electricity.For many services, consumers have to pay more for electricity in periods of high peak demand.Consequently, if consumers have knowledge about the expected peak load ahead of time, such extra charges may be avoided.Accurate forecasting of energy demand and, therefore, information on expected peak loads will not only help to provide a reliable supply of electricity, but it can also be useful in reducing the cost of electricity at the consumer level.In this paper, we develop a comparative study for aggregated short-term load forecasting using different data strategies and comparing two prediction levels : the first one aims to predict the aggregated load using a whole district data set, while the second one focuses on performing predictions on a lower level then aggregating these predictions at the district level.After finding the best forecasting model and strategy, these accurate predictions will help us predict the percentage of peak over a certain subscribed power in the entire district.For the load prediction, we obtain a mean absolute percentage error between 1.83% and 5.18% depending on the prediction horizon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 it