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Record W2507202755 · doi:10.5281/zenodo.61494

Decision Support for Urban Wind Energy Extraction

2007· article· en· W2507202755 on OpenAlexaffabout
Ruth Cooper

Bibliographic record

VenueINFM-OAR (INFN Catania) · 2007
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExtraction (chemistry)Wind powerEnvironmental scienceComputer scienceMeteorologyGeographyEngineering

Abstract

fetched live from OpenAlex

The assessment of urban morphology-induced wind amplification, specifically in support of siting building-integrated and/or mounted UWECSs, is a very recent undertaking. Only a small number of wind turbine manufacturers are even exploring the development of suitably-scaled devices whose performance characteristics are tailored to urban wind conditions. As such, this research explored the feasibility of BAWT-theory in an urban setting through the development of a prototype Urban Wind Energy Planning (UWEP) Decision Support System (DSS). The prototype UWEP DSS focuses primarily on building aerodynamics-induced wind amplification, including consideration of peak-wind seasons. Microsoft® Excel was selected as the platform for the UWEP DSS, supporting development of user forms and integral databases. This tool is intended for a broad range of users, including the average home owner, UWECS developers, and energy planners. With minimal user input, the UWEP DSS determines the mean wind speed within the amplification zones, the location of the amplification zones, and the energy that could potentially be generated by an appropriately-sited UWECS. Two case study applications of the UWEP DSS were conducted to validate the estimations and demonstrate the capabilities of the tool. The University of Toronto Robarts Library application and the Green Venture EcoHouse application yielded credible mean wind speed and potential wind energy estimates on comparison to the online wind atlases. The EcoHouse case study application included the selection and siting of various UWECSs. It highlighted the potential of a hypothetical wind energy conversion device being able to generate almost 40% of the 700 kWh per month, average household energy demand. Conversely, it demonstrated that the traditional tower-mounted horizontal axis wind turbines, situated outside of the potential amplification zones in accordance with current siting guidelines, would only be able to generate 5% of the demand. By demonstrating the prototype UWEP DSS through an institutional and a residential application case study, it is hoped that the scope and capabilities of, and the amplified wind energy potential identified by, this tool will foster further research in urban wind energy planning, building aerodynamics-induced amplification assessment, and development of new UWECSs. The prototype UWEP DSS appears to be the first to estimate building aerodynamics-induced amplification from peak composite pressure-gust coefficients published in building codes. Further research is recommended to gain a better understanding of sustained, as opposed to peak, wind amplification. The modular nature of the UWEP DSS lends itself to the modifications that will undoubtedly be required as further knowledge is developed in this field.

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)0.004
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0290.004

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 head0.013
GPT teacher head0.246
Teacher spread0.234 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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