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Record W2930274925 · doi:10.3390/su11071930

Optimal Design of a Distributed Energy System Using the Functional Interval Model That Allows Reduced Carbon Emissions in Guanzhong, a Rural Area of China

2019· article· en· W2930274925 on OpenAlexaff
Ying Zhu, Quanling Tong, Xueting Zeng, Xiaxia Yan, Yongping Li, Guohe Huang

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsInterval (graph theory)Environmental economicsElectricityChinaElectricity generationGridEnergy supplyEnvironmental scienceComputer scienceOperations researchEnvironmental engineeringPower (physics)EngineeringEnergy (signal processing)EconomicsMathematicsGeographyElectrical engineeringStatistics

Abstract

fetched live from OpenAlex

Nowadays, rural power supply in China plays an important role in restricting the economic development and improvement of residential living standards. In this study, an interval full-infinite programming rural energy model (IFIP-REM) was developed for supporting distributed energy system (DES) optimal design under uncertainties in rural areas. By affecting the upper and lower bounds of the interval by complex and variable external conditions, IFIP-REM could simulate the influence of external systems. To validate the model, a real case study of DES optimal design in Guanzhong, a rural area of China, was tested and aimed to minimize system cost and constraints of resources, energy supply reliability, and carbon emission mitigation. The data revealed generation of reasonable optimization schemes to obtain interval solutions of IFIP-REM. Compared to centralized energy system (CES), DES reduced electricity purchasing of the municipal grid by 47.5% and extended carbon emission of both upper and lower bounds to [17.13, 44.51] % and [12.42, 36.02] %, respectively. Overall, the proposed model could help managers make decisions of DES optimal design by coordinating conflicts among economic cost, system efficiency, and carbon emission mitigation.

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: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.675

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 head0.013
GPT teacher head0.212
Teacher spread0.198 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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