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Record W2021055895 · doi:10.1080/15567240701231958

Integrated Capacity Planning for Electricity Generation: A Fuzzy Environmental Policy Analysis Approach

2008· article· en· W2021055895 on OpenAlex
Fuzhan Nasiri, Guohe Huang

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnergy Sources Part B Economics Planning and Policy · 2008
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFuzzy logicElectricityEnvironmental economicsComputer scienceElectricity generationBusinessOperations researchEnvironmental planningEnvironmental scienceEngineeringEconomicsArtificial intelligencePower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Abstract This study proposes an integrated model for capacity planning in electricity generation. It utilizes a multiple-criteria linear programming to incorporate cost and environmental objectives into the planning. To treat the uncertainties embedded in definition of model parameters, the concept of decision-maker degree of optimism will be used. Optimization of the model provides different planning scenarios. To determine the best compromise plan, a post-optimization assessment based on fuzzy set theory concepts is developed. The proposed methodology is employed for a medium-term capacity planning in Canada's electricity generation sector. The results approve a major capacity growth for natural gas facilities accompanied by retirement of most coal-burning facilities.

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: Empirical
Teacher disagreement score0.063
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
Bibliometrics0.0010.000
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 head0.029
GPT teacher head0.203
Teacher spread0.174 · 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