Machine learning for assessing variability of the long-term projections of the hydropower generation on a European scale
Bibliographic record
Abstract
A big challenge of sustainable power systems is the integration of climate variability into the operational and long-term planning processes. In this paper, we focus on the run-of-river based hydropower generation on a European scale. In particular, we deal with the modeling of this form of power production based on climate variables. Translating time series of climate data (precipitation and air temperature) into time series of run-of-river based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. Indeed, this kind of electricity generation is limited by the flow of the river in which the power plants are located. Moreover, the water flow is a nonlinear function of the climate variables and the geographical characteristics of the river basins. Finally, the impact of the climate variables on the runoff may occur with a certain delay, whose determination depends on physically based phenomena (e.g., melting snow-local temperature). In this work, we first compare well-established machine learning regression algorithms to be used for modeling the run-of-river hydropower generation. Then, the technique showing to have the best performance is used for producing long-term estimates of hydropower capacity factors based on future climate scenarios for each European country.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".