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Record W2050071282 · doi:10.1109/tsg.2014.2373271

Optimal Operation of Industrial Energy Hubs in Smart Grids

2014· article· en· W2050071282 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2014
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSmart gridScheduling (production processes)Energy managementDemand responseOperator (biology)Load managementEnergy management systemComputer scienceIndustrial productionProcess (computing)EngineeringMathematical optimizationOperations researchIndustrial engineeringElectricityEnergy (signal processing)Electrical engineeringOperations management

Abstract

fetched live from OpenAlex

This paper presents the development of a generic optimal industrial load management (OILM) model, which can be readily incorporated into energy hub management systems (EHMSs) for industrial customers, in interaction with local distribution companies (LDCs), for automated and optimal scheduling of their processes. The mathematical models comprise an objective function to minimize the total energy costs and/or demand charges for industrial customers, and a set of equality and inequality constraints to represent the industrial process, storage units, distribution system components, operator's requirements, and other relevant constraints. The effectiveness of the proposed OILM model is demonstrated in two industrial customers: 1) a flour mill; and 2) a water pumping facility. The results show that the proposed OILM model, in conjunction with communication and control infrastructures at the customer and LDC levels, would allow optimal operation of industrial EHMSs in smart grids.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.782
Threshold uncertainty score1.000

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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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