Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production
Bibliographic record
Abstract
Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the Netherlands at different sampling frequencies: 15 min and 60 min. Specifically, we assess the performance of the convolutional neural networks (CNNs), temporal CNN (TCNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), and the deep neural network (DNN). In addition, the standard machine learning models, namely the k-nearest neighbors (kNN) algorithm and decision trees (DTs), are adopted as baseline predictive models. Bayesian optimization is applied for hyperparameter tuning across multiple models. In total, 54 experimental tasks were performed. For the electricity load at 15 min intervals, the DT shows exceptional performance, while for the electricity load at 60 min intervals, DNN performs the best, in general. For wind generation at 15 min intervals, DT is the best performer, while for wind generation at 60 min intervals, both DT and TCNN provide good results, in general. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".