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Record W2539874559 · doi:10.1109/epc.2007.4520313

Electricity Usage in Water Distribution Networks

2007· article· en· W2539874559 on OpenAlexaffabout
Gaurav Kumar, Bryan Karney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeaking power plantElectricityLeakage (economics)Electricity generationLeverage (statistics)Energy conservationEnergy storageEnvironmental scienceElectric potential energyDistributed generationPumped-storage hydroelectricityMains electricityWater conservationElectric power distributionEnvironmental economicsEngineeringRenewable energyPower (physics)Computer scienceElectrical engineeringWater resourcesEconomicsPhysics

Abstract

fetched live from OpenAlex

The electricity usage with respect to water distribution systems is examined for the particular case of Ontario. The case is made that the integration of conservation and energy efficiency measures between the utilities would leverage investments better than isolated efforts. The similarities between water and energy production are highlighted and potential energy savings for water distribution systems are presented. Preliminary calculations of these savings yielded a 11- 27 MW power reduction over the daily cycle for leakage protection. These values were derived from the gross, yet conservative total of 57 MW, if all the leakage in hydraulic conduits was contained. Electrical load shifting (peak shaving) through active pumping for water distribution systems yielded a potential between 450 and 100 MW of freed up generation. It should be noted that the higher value of 450 is still conservative and that this could be significantly higher for the province if distributed storage was actively implemented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.128

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.170
Teacher spread0.167 · 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.

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

Citations10
Published2007
Admission routes2
Has abstractyes

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