Role of hydrologic information in stochastic dynamic programming: a case study of the Kemano hydropower system in British Columbia
Bibliographic record
Abstract
This paper presents a study describing the effect of various hydrological variables in stochastic dynamic programming (SDP) for solving the optimization problem of managing a hydropower system. We will show how choosing the best hydrological variables can strongly affect management policies. This is especially true for the system studied here, namely the Kemano hydroelectric system located in British Columbia, Canada, which is subject to large streamflow volumes due to significant snow cover during winter. Real-time snow water equivalent (SWE) data can be used directly as a variable in SDP management policies. Results indicate that for the system in this study, the maximum SWE (i.e., highest level of SWE observed from the start of winter to the current decision period) is the best among the methods investigated for effective, safe management, compared with Markov or order p autoregressive models when forecasts are not available.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".