Integration of Hydro and Renewable Energy Resources in Energy Planning
Bibliographic record
Abstract
Abstract Renewable energy is the energy obtained from resources inexhaustible in the long term. Furthermore, in some countries, non-conventional renewable energy includes solar, wind, biomass, geothermal and mini-hydropower. The definition of mini-hydropower plants varies depending on the country. As an example, in Peru and Canada, mini-hydropower plants have different installing capacities, below 20MW and 50MW, respectively. Accordingly, this work (i) discusses the Energy Balance and challenges that renewable energies have to face on their way to the energy transition, (ii) highlights the forecast models to generate renewable energy in short-term energy planning. The historical data about the renewable energy resources and the energy produced have been obtained by COES. The R studio software was used for statistical analysis of renewable resources and electricity generation. Also, a forecast model was developed using a neural network to forecast renewable energy generation. The results show a strong correlation between hydro resources and non-conventional renewable energy resources. Finally, the data obtained from the renewable generation forecast model were used as input to carry out a short-term dispatch model using GAMS software to determine the forecast of daily marginal cost in SEIN.
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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".