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Record W4234541337 · doi:10.24124/2020/59103

Assessing offshore wind power resources in British Columbia

2020· dissertation· en· W4234541337 on OpenAlexafffundabout
Talaat Bakri

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Northern British Columbia
FundersNational Renewable Energy LaboratoryUniversity of Northern British ColumbiaNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaEnvironment and Climate Change CanadaNational Oceanic and Atmospheric AdministrationPacific Institute for Climate Solutions
KeywordsWind speedMaximum sustained windOffshore wind powerWind powerMeteorologyDownscalingEnvironmental scienceMesoscale meteorologyClimatologyWind wave modelPrevailing windsWind shearWind directionSubmarine pipelineWind profile power lawSpatial variabilityWind gradientGeologyGeographyPrecipitationOceanography

Abstract

fetched live from OpenAlex

Wind resources are investigated and estimated offshore of the northern and central coasts of British Columbia, Canada. Remote sensing-based wind speed observations from a Synthetic Aperture Radar (SAR) mounted on the Canadian RADARSAT-2 satellite are used for mapping offshore winds. In addition, in-situ wind speed observations extracted from several buoys distributed in the study region are used to analyze the temporal and spatial wind speed variations in relation to wind power generation. Sustained winds above several wind turbine thresholds are analyzed and values of 50-yr and 100-yr return extreme wind speed levels are calculated. The wind variability analysis suggests few interruptions to power generation by either very low wind speeds or extreme wind speed events with high spatial variability between offshore areas and sites located within the coastal mountains. The SAR wind speed fields are characterized by a high spatial resolution but cover a period of less than 2.5 years with a random temporal availability. The SAR fields are extrapolated to reanalysis long-term wind fields that are available over a climatological time period with a sub-daily temporal resolution but a coarse spatial resolution. The extrapolation procedure is developed by applying a statistical downscaling model and a bias-based correction method. Wind fields from both methods are validated against the in-situ observations from buoys. The extrapolated wind fields are used for mapping offshore winds by creating a robust wind climatology that represents the mesoscale wind variance as well as the diurnal wind variability. This wind climatology is used to calculate the wind statistics and power density, in addition to estimate offshore wind resources. Viable areas for wind power development are defined by using high resolution bathymetric data and considering the general environmental and ecological constraints in the region. The estimated offshore wind resource energy using only theiv determined viable areas is found to resemble a large portion of the current total power generation in British Columbia. Most suitable areas for offshore wind farms are determined by developing criteria based on a combination of the turbine tower technology, water depth zoning and power density values.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designObservational
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
Published2020
Admission routes3
Has abstractyes

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