Regression Learner Application Model-Based Short-Term Load Forecasting for Mascouche (Quebec, Canada)
Bibliographic record
Abstract
Load forecasting is crucial for power systems optimal operation and allows power utilities to overcome technical and economic issues. Some forecasting techniques are currently being deployed on a large scale to meet the requirements of increased energy demand while balancing it with the production to achieve socio-economic benefits for sustainable development. In this paper, we are diving into the forecasting using the regression method. We are focusing on short-term load forecasting and how it can give businesses valuable insights into future sales, labor needs, and more. Power utilities use short-term load forecasting technology to make reasonable power systems. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators leading to models’ optimization. To make things real, we are using actual load and weather data from the Hydro-Quebec database. We will be exploring the capabilities, advantages, and limitations of this method, all while keeping an eye on the changing landscape of electricity supply and demand. Our study is centered around the Mascouche region in Quebec, Canada, where the load fluctuates between 60 to 140 megawatts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".