Hourly Hydropower Production Forecasting with Machine Learning: A Case Study in Linköping, Sweden
Bibliographic record
Abstract
Machine Learning (ML) is frequently utilized in prediction tasks; however, its applications in hydropower forecasting, particularly in forecasting hourly power production, has not been thoroughly investigated.In this paper, two Deep Learning (DL) models, namely an autoregressive neural network and Long Short-Term Memory, are compared to a seasonal autoregressive moving average (SARIMA) model to forecast the hourly power production at a hydropower station situated in Linköping, Sweden.Hyperparameter optimization algorithms are used to identify suitable DL models and algorithms for automatic model identification of SARIMA models are utilized.The three models are evaluated using a rolling origin strategy on a test dataset that consists of 10 months (January -October 2023) of hourly power production.The DL models provided similarly accurate forecasts as the SARIMA model according to mean squared error and mean absolute error.However, the DL models are poorly calibrated, resulting in lower coverage compared to the SARIMA model.Furthermore, the models are using a univariate time series (i.e., using historical power production to forecast future power production) and future studies need to explore additional variables that may be useful in providing a more accurate forecast.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".