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Record W3169256824

Energy Commitment: A Planning of Energy Carrier Based on Energy Consumption

2019· article· en· W3169256824 on OpenAlexaff
Mohammad Dehghani, Zeinab Montazeri, O.P. Malik

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2019
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEnergy consumptionEnergy (signal processing)Energy carrierEnergy accountingEnergy engineeringEnergy balanceEnergy supplyEnvironmental economicsEfficient energy useEnergy conservationEnergy policyRenewable energyEngineeringEconomicsElectrical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Energy consumption is one of the criteria for determining the quality of life in a country. Continued supply of energy and the possibility of long-term access to resources require a comprehensive plan. One of the key issues in the field of energy planning is energy carriers. In this paper, a new theory is introduced to energy network studies for planning of energy carriers called Energy Commitment. In this theory, an appropriate planning is applied for energy carriers based the final energy consumption. Energy carriers are available either naturally or after the energy conversion process. Energy commitment is modeled on an energy network with the presence of electrical energy, gas energy, transportation section, agriculture section, industrial section, residential section, commercial section, and general section. References 25, tables 3.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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
Published2019
Admission routes1
Has abstractyes

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Same venueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogySame topicSmart Grid Energy ManagementFrench-language works237,207