Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A myriad of techniques regarding renewable energy forecasting have been proposed in recent literature, commonly classified as physical, statistical, machine learning based or a hybrid form thereof. The renewable energy forecasting process is however elaborate and consists of multiple stages, where different approaches from these four categories apply variably, complicating a holistic classification of the process. This paper resolves this by utilizing the fundamental difference between direct and indirect forecasting in terms of model complexity, data availability, spatial and time horizons as the backbone to structure this intricate forecasting process. As such, a significant step towards a generalized framework for renewable energy forecasting is presented. Additionally, a most promising recommendation emerges: leveraging physics-based knowledge from indirect models to enhance training of direct methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it