Machine Learning and Optimization Model Development for Northern Community Energy Planning
Bibliographic record
Abstract
Many Arctic communities are currently transitioning away from diesel-based electricity to renewable energy systems, a challenge which requires resource assessment of suitable generation technologies as well as community energy planning. While power estimation models are readily available for wind and solar PV, few accessible models exist for hydrokinetic power prediction, a river-based technology that is currently being piloted in Alaska. To improve hydrokinetic resource assessment, Chapter 2 in this paper develops a predictive model through a machine learning framework to remotely estimate stream velocity. A Random Forest model was found to outperform the traditional Manning equation approach by estimating velocity on a small dataset with a mean absolute percent error of 22%, a 52% improvement in prediction accuracy over the Manning equation, which reported a mean absolute percent error of over 46%. A more generalizable model that was trained on a larger dataset and included additional, novel geometric input variables was found to predict stream velocity with a mean absolute percent error of 24%. This model demonstrated advantages over existing models that either required in-situ data collection or were not compatible with smaller streams suitable for community-level energy planning.
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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 | 0.001 | 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".