The Impacts of Wind Speed Trends and 30-Year Variability in Relation to Hydroelectric Reservoir Inflows on Wind Power in the Pacific Northwest
Bibliographic record
Abstract
In hydroelectric dominated systems, the value and benefits of energy are higher during extended dry periods and lower during extended or extreme wet periods. By accounting for regional and temporal differences in the relationship between wind speed and reservoir inflow behavior during wind farm site selection, the benefits of energy diversification can be maximized. The goal of this work was to help maximize the value of wind power by quantifying the long-term (30-year) relationships between wind speed and streamflow behavior, using British Columbia (BC) and the Pacific Northwest (PNW) as a case study. Clean energy and self-sufficiency policies in British BC make the benefits of increased generation during low streamflow periods particularly large. Wind density (WD) estimates from a height of 10m (North American Regional Reanalysis, NARR) were correlated with cumulative usable inflows (CUI) for BC (collected from BC Hydro) for 1979-2010. The strongest WD-CUI correlations were found along the US coast (r ~0.55), whereas generally weaker correlations were found in northern regions, with negative correlations (r ~ -0.25) along BC's North Coast. Furthermore, during the lowest inflow years, WD anomalies increased by up to 40% above average values for the North Coast. Seasonally, high flows during the spring freshet were coincident with widespread negative WD anomalies, with a similar but opposite pattern for low inflow winter months. These poorly or negatively correlated sites could have a moderating influence on climate related variability in provincial electricity supply, by producing greater than average generation in low inflow years and reduced generation in wet years. Wind speed and WD trends were also analyzed for all NARR grid locations, which showed statistically significant positive trends for most of the PNW and the largest increases along the Pacific Coast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".