An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset
Bibliographic record
Abstract
Wind and solar energy resources are an increasingly large fraction of generation in global electricity systems. However, the variability of these resources necessitates new datasets and tools for understanding their economics and integration in electricity systems. To enable such analyses and more, we have developed a free web-based tool (Global Renewable Energy Atlas & Time-series, or GRETA) that produces hourly wind and solar photovoltaic (PV) generation time series for any location on the globe. To do so, this tool applies the Boland–Ridley–Laurent and Perez models to NASA’s (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) solar irradiance reanalysis dataset, and the Archer and Jacobson model to the MERRA wind reanalysis dataset to produce resource and power data, for a given technology’s power curve. This paper reviews solar and wind resource datasets and models, describes the employed algorithms, and introduces the web-based tool.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".