Analyzing the Performance of Direct and Indirect Net Load Forecasting Strategies Under Varying Penetration Levels of PV and Wind Power
Bibliographic record
Abstract
The increasing share of wind and photovoltaic (PV) power in modern power systems requires accurate net load (NL) forecasts for keeping a stable and economic power system operation. Researchers have been focusing on which net load forecasting (NLF) method can give the best possible accuracy and on whether it can be achieved through the direct or the indirect net load forecasting strategy (NLFS). The studies depended on datasets taken from particular power systems having a specific renewables penetration level (PL). In other words, the obtained accuracy levels were not validated on non-identical power systems and dissimilar renewables’ PLs. This research takes a different route by examining the dependency of the net load forecasting accuracy (NLFA) on wind and PV power PLs, the power system nature and the NLFS. Thus, we employed datasets from two different power systems: New England (NE) and Spain and forecasted the NL over four seasons of the year using both the direct and the indirect NLFSs. Besides the actual data, we assumed additional scenarios representing potential increase in either wind power, PV power or both and forecasted the NL in all of the cases and then compared the results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".