Time series forecasting based on multi-criteria optimization for model and filter selection applied to hydroelectric power plants
Bibliographic record
Abstract
The power generation management in power systems based on water resources depends on the level of the reservoirs at the hydroelectric plants. Considering the advances in machine learning, forecasting inflow variation using time series could be an alternative for improving power system management. Given that there are several forecasting models and filters that can be applied, choosing one can be a challenging task, requiring experience from the designer. To solve this, multi-criteria optimization for selecting the models using the tree-structured Parzen estimator approach is proposed in this paper. The study considers the inflow data from the Belo Monte dam in Brazil. The multi-layer Elman recurrent neural network (RNN), dilated RNN, long short-term memory (LSTM), temporal fusion transformer (TFT), temporal convolutional neural (TCN), deep non-parametric time series (DeepNPTS), neural basis expansion analysis for time series (N-BEATS), and neural hierarchical interpolation for time series (NHITS) models are considered. The Christiano-Fitzgerald, Hodrick–Prescott, season-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL filters are used. The proposed method, based on hypertuned TFT with the Hodrick–Prescott filter, had a mean absolute percentage error (MAPE) of 0.02 and a symmetric MAPE of 1.99, being superior to all the compared structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".