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Record W7053085955

Twenty-first century wind and solar energy potential in northern Canada

2021· dissertation· en· W7053085955 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyClimate changeWind powerClimate modelSolar powerElectric power systemSolar energyElectricity generation
DOInot available

Abstract

fetched live from OpenAlex

Northern regions of Canada are of special interest for renewable energy investment due to the high cost of traditional energy generation in remote communities (Das & Canizares 2016b). However, climate variability and change have a substantial impact on renewable energy yield and system vulnerability (e.g., Ravestein et al. 2018; van der Wiel et al. 2019), and the North will experience more dramatic impacts due to climate change compared with other parts of Canada (Serreze 2015). Using the Canadian Regional Climate Model Version 4 (CanRCM4) large ensemble driven by Representative Concentration Pathway 8.5, current and future wind and solar energy potential, variability and covariability in northern Canada were assessed. Eight focal communities were additionally selected for in-depth analysis based on the work of Das & Canizares (2016b). Robust increases in annual average wind power potential (WPP) are projected across the northernmost part of the study area by 2070-2099 (up to 30%), with changes most pronounced in cold seasons. Decreases in WPP are projected for southern areas. Solar power potential (SPP) is projected to decrease across the study area, with robust changes emerging by as early as 2010-2039. For the focal communities, WPP stability (as measured through inter- and intra-annual variability) is projected to increase, while SPP stability is projected to decrease. The changes in WPP variability are associated with a dampening of the seasonal cycle of WPP in the north. Monthly mean WPP and SPP are negatively correlated, with approximately oppositely-phased seasonal cycles. Combined wind/solar installations therefore show reduced sub-annual variability, stabilizing power supplies relative to installations of solely wind or solar power. Drivers of change in WPP and SPP are complex, but changes in sea ice across the 21st century will play an important role for both WPP and SPP. Over the northern ocean regions, the influence of sea ice loss on roughness length is found to be more important than impacts on surface layer stability. Changes in storm winds also play a role, but impacts due to synoptic storm activity are difficult to distinguish from shifts in the wind speed distribution. Decreases in SPP can be attributed to projected reductions in downwelling shortwave radiation, which in turn are closely associated with changes in cloud characteristics (e.g., optical depth), as measured through CanRCM4 column liquid/ice water content. Clear-sky changes in shortwave radiation were not directly assessed, but are potentially impacted by robust increases in column water vapour. Overall, northern regions of Canada and the focal communities show high potential for renewable energy generation across the 21st century. Projected increases in wind power resources and wind power stability in the focal communities could enhance the cost-savings and emissions reductions predicted based on current climate assessments (e.g., Das & Canizares 2016b). With ever improving technologies and declining costs, the viability of renewable power in the north is likely to become even more certain in years to come.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.006
GPT teacher head0.217
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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