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Record W2947947931 · doi:10.11575/prism/35917

Pre-feasibility Study Of District Energy In The West Campus Development

2014· article· en· W2947947931 on OpenAlexaboutno aff
Philippe Dufour, Krupa Mistry, Rheanne Ritchie

Bibliographic record

VenuePRISM (University of Calgary) · 2014
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy (signal processing)Environmental planningGeographyStatistics

Abstract

fetched live from OpenAlex

District Energy (DE) stands as a promising technology, which has environmental, energy, and economic benefits. DE has the potential to reduce our dependence on centralized energy production, carbon-intensive power generation, and also alleviates significant infrastructure cost at the building and community levels. Currently, centralized electricity generation creates system inefficiencies, which are both wasteful and expensive elements of status quo operations. Building specific downstream heating and cooling infrastructure poses similar liabilities. While DE systems are currently being used in a variety of urban applications with the goal of avoiding these system inefficiencies altogether, these systems do require a master-planned implementation in most cases, which makes retrofitting a challenging endeavor. The objective of this report is to perform a pre-feasibility study as to whether DE can be implemented into the West Campus Development (WCD), and how that implementation ought to be performed. The first chapter of this report describes the research intent, DE and combined heat and power as technologies, as well as the WCD concept, and the basis for most of this report’s data; the University of Calgary’s central plant and DE network. The central chapters (2, 3, & 4) of this report outline the benefits of DE pertaining to energy, the environment, and economics respectively, with each chapter concluding with WCD case-specific benefits. The final chapters offer recommendations and a conclusion for the case. Most of the report uses data and implementation concepts obtained from leading DE systems in both Canada and the United-States. Additionally, hard data comes from the University of Calgary’s own central plant. Currently known load profiles, natural gas usage and electricity consumption values were applied to specific land use types in WCD. The highest density development within the WCD, known as the ‘density hub’, is the focus of the DE network implementation.

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)0.011
metaresearch head score (Gemma)0.013
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 score0.029
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.175
Teacher spread0.166 · 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
Published2014
Admission routes1
Has abstractyes

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