A decision-support system for optimal operation of hydropower stations /
Bibliographic record
Abstract
Hydro-Quebec utilizes a one-dimensional hydrological model called H2RM to simulate the evolution of flow on harnessed rivers. The model produces reliable results but its application is restricted to so-called 'hydrological domains', that define the flow routing pattern in-between hydroelectric facilities. When many power stations are present on a river, each hydrological domain must be treated separately, then linked to the next one. No information is provided on storage in head reservoirs nor electric production from power stations. A computer program (shell) has been developed to simulate a complete hydropower system comprising a number of head reservoirs, hydrological domains and power stations. In this program, head reservoir behavior is reproduced by computing a mass balance, flow routing within a hydrological domain is simulated using the H2RM model, and production from each power station is estimated through an optimization procedure. The shell program can be used as a decision support tool by allowing the comparison between various water management schemes and by displaying stage and discharge at any point of the hydropower system, storage in head reservoirs, and optimal power output from the turbines for the head and flow conditions prevailing at each power station. Examples of application are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".