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Record W3165380363 · doi:10.11575/prism/35998

Impact Of Demand Side Management On The Residential Electricity Use In The City Of Calgary

2017· article· en· W3165380363 on OpenAlexaboutno aff
Andun Jevne

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsDemand sideElectricityBusinessElectricity demandDemand managementOperations managementEnvironmental planningNatural resource economicsEnvironmental economicsGeographyEconomicsEngineeringElectricity generationPower (physics)

Abstract

fetched live from OpenAlex

Under the Climate Leadership Plan, Alberta is beginning a significant transition in how it generates electricity. The anticipated phase out of coal fired power production combined with an increase in renewable generation capacity is expected to increase consumer costs for electricity in the near future. This study predicts that for the City of Calgary, demand side management measures could have a positive impact on the electricity system by reducing peak consumption, reducing average daily consumption, and reducing emissions produced through electricity generation. This could translate to savings at the household level as well as on a larger systems level in potentially delaying the need to replace or upgrade current generation, transmission, and distribution infrastructure. In particular, the demand side management method of In-Home Displays provides a cost effective way to achieve desired reductions, reducing costs to consumers, and improving the public’s energy literacy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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 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
Published2017
Admission routes1
Has abstractyes

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