MétaCan
Menu
Back to cohort
Record W3082509915

Optimal Distributed Generation Alocation to Find Optimal Voltage Profile With MinimumDG Investment Cost in a Smart Neighborhood

2020· article· en· W3082509915 on OpenAlexaboutno aff
Mohammadreza Fathi

Bibliographic record

VenueJournal of Chemical Biology & Pharmaceutical Chemistry · 2020
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyUpgradeArchitectural engineeringRetrofittingEfficient energy useProcess (computing)Energy engineeringZero-energy buildingInvestment (military)EngineeringEnvironmental economicsComputer scienceRisk analysis (engineering)BusinessEconomics
DOInot available

Abstract

fetched live from OpenAlex

When talking about the effect of renewable energy, our thinking can be exemplified by an action of “placing an icing on the cake”. We must have a good building, to which we add renewable energy sources. The authors however, reverse the traditional design process and starting an integrated design process, we ask the question – how can we design an affordable, energy efficient building that the effect of the renewable energy sources is reinforced? We start with a system that must fulfill several technical requirements and one of the synergies in the design process will be to effectively incorporate the renewable energy sources. Changing the paradigm of design is the result of actual construction development in countries like Canada, USA and Japan and while we are looking at this trend from the scientific point of view, we are also be able to illustrate the science behind the next generation of the construction retrofitting with practical examples from these three counties. In effect, this short note becomes a conceptual progress report on energy efficiency in thermal upgrade of buildings. Keywords: energy efficiency; building automatic control; energy use under field conditions; two-stage construction process; cost-benefit evaluation; deep retrofit of residential buildings

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.691

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 head0.022
GPT teacher head0.264
Teacher spread0.241 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chemical Biology & Pharmaceutical ChemistrySame topicBuilding Energy and Comfort OptimizationFrench-language works237,207