Machine learning power system emulation for rapid screening of multi-sector policies
Bibliographic record
Abstract
Linking integrated water-energy simulation with multi-objective search algorithms provides a practical design tool for interdependent river basins and power systems. However, this approach is typically limited by the computational resources required to complete the many thousands of simulations to discover efficient solutions. We introduce an artificial neural network-based power system emulator to enable optimized design of large-scale detailed multi-sector water-energy systems. The proposed framework links an integrated power system emulator and river system simulator to an AI-driven multi-objective search design process. We compare optimized designs using both the power system emulator and simulator to check the emulators’ computational speed and accuracy. The framework is applied to the Sudanese power system and its link to the Eastern Nile river basin, to investigate how optimized operational strategies of the Grand Ethiopian Renaissance Dam (GERD) could affect Sudan’s resource systems. Results are similar for the power system emulator and simulator, showing the emulator helps to significantly reduce the computational cost of using sophisticated multi-sector policy design approaches.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".