Forecasting Electricity Load and Wind Generation: A Comparative Analysis of Machine Learning Models Enhanced by Bayesian Optimization under Different Sampling
Bibliographic record
Abstract
The study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data, focusing on the optimization and comparative analysis of various models. Given the critical importance of accurate energy forecasting in managing power grids and integrating renewable energy sources, this research seeks to enhance forecasting precision through the application of Bayesian optimization for hyperparameter tuning across multiple models. Utilizing time-series data, this study systematically evaluates the performance of several predictive models. Each model's parameters were meticulously optimized using Bayesian techniques to identify the most effective configurations for handling the complex dynamics of energy data. The research methodology involved a comparison within single datasets to identify the best model. Subsequently, the best-performing models were further analyzed across different datasets to validate their robustness and generalizability. The primary evaluation metric is the Root Mean Squared Error (RMSE), complemented by additional metrics to provide a comprehensive assessment of model accuracy and effectiveness. Key findings demonstrate that while some models excel in capturing overall trends, challenges remain in addressing the volatility and variability inherent in the data. 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 | 0.010 | 0.019 |
| 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 | 0.002 |
| 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".