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Record W2096319640 · doi:10.1109/epec.2009.5420813

BC Hydro's methodology for energy losses assessment in distribution systems

2009· article· en· W2096319640 on OpenAlexaff
J. Peralta, Alice Cheung, M. Pedley, Jing Tao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsRisk analysis (engineering)Energy conservationComputer scienceDistribution (mathematics)Reliability engineeringEnergy (signal processing)Environmental economicsEngineeringBusinessEconomics

Abstract

fetched live from OpenAlex

Current utility business and regulatory environments impose new demands and constraints on the development, operation, and maintenance of distribution systems. There is increasing pressure to ensure that all aspects of distribution utility operations are appropriately and optimally managed. The strong interest in achieving efficiency targets related to energy conservation is encouraging utilities to incorporate and implement more rigorous investments plans for distribution losses reduction. For this reason, utilities need to understand the cause of the energy losses, the methodologies to assess them, and the measures to reduce them in a cost-effective manner. This paper presents a methodology for assessing technical and non-technical energy losses in distribution systems that has been successfully implemented at BC Hydro. It also discusses techniques to manage and reduce energy losses.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.028
GPT teacher head0.299
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations5
Published2009
Admission routes1
Has abstractyes

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