Weather-Driven Complementarity between Daily Energy Demand at One Location and Renewable Supply at Another
Bibliographic record
Abstract
Abstract In a future world where most of the energy must come from intermittent renewable energy sources such as wind or solar energy, it would be more efficient if, for each demand area, we could determine the locations for which the output of an energy source would naturally match the demand fluctuations from that area. In parallel, meteorological weather systems such as midlatitude cyclones are often organized in a way that naturally shapes where areas of greater energy need (e.g., regions with more cold air) are with respect to windier or sunnier areas, and these are generally not collocated. As a result, the best places to generate renewable energy may not be near consumption sites; these may be determined, however, by common meteorological patterns. Using data from a reanalysis of six decades of past weather, we determined the complementarity between different sources of energy as well as the relationships between renewable supply and demand at daily averaged time scales for several North American cities. In general, demand and solar power tend to be slightly positively correlated at nearby locations away from the Rocky Mountains; however, wind power often must be obtained from greater distances and at altitude for energy production to be better timed with consumption. Significance Statement Weather patterns such as high and low pressure systems shape where and when energy is needed for warming or cooling; they also shape how much renewable energy from winds and the sun can be produced. Hence, they determine the regions where more energy is likely to be available in periods of unusually high need for each demand location. Finding where those areas are may result in more timely renewable energy production in the future to help reduce fossil fuel use for energy production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".