Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review
Bibliographic record
Abstract
The global trend toward a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy various demands of the market. Successful and steady integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables' stochastic behaviors. In a few reported literature studies, machine learning- (ML-) based forecasters have been widely utilized for wind power and solar power forecasting with promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind power and solar power ML forecasters, namely artificial neural networks (ANNs), recurrent neural networks (RNNs), support vector machines (SVMs), and extreme learning machines (ELMs). In addition, special attention is paid to metaheuristics accompanied by these ML models. Detailed comparisons of the different ML methodologies and the metaheuristic techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on the ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.003 | 0.001 |
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".