The Challenge Posed by Space Weather to High‐Voltage Electricity Flows: Evidence From Ontario, Canada, and New York State, USA
Bibliographic record
Abstract
Abstract In this paper, we present evidence that geomagnetic activity has the potential to disrupt the electricity flows between Ontario, Canada, and New York State, USA. This conclusion is based on a modeling framework that makes use of weather data, electricity load data, measures of transmission “network effects,” proxies for geomagnetically induced currents and the expected level of power grid conditions. The model is estimated using hourly data throughout 1 May 2002 through 31 October 2003. The model is evaluated using out‐of‐sample hourly data over the period of 1 November to 9 December 2003. The out‐of‐sample predictions are more accurate when the forecasting equation reflects the estimated contribution of geomagnetic activity. The structural modeling results for the sample period indicate that the peak predicted effect of geomagnetic activity on the electricity flow was about 1,604 MWh in absolute value when the geomagnetically induced current proxy achieved a value of 363.2 nT/min. The analysis also indicates that the level of the geomagnetically induced electricity flow is highly dependent on ambient temperature and expected system conditions at the time of the geomagnetic storm. Moreover, geomagnetic activity is an important driver of the volatility in the electricity flows. The overall findings indicate that the scope of the challenge posed by space weather to the operations of the electric power system is likely understated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".