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Record W4388617784 · doi:10.1080/23744731.2023.2279469

Variable refrigerant flow heat pump model with estimated parameters and emulated controller based on manufacturer data

2023· article· en· W4388617784 on OpenAlex
Aziz Mbaye, Massimo Cimmino

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".

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience and Technology for the Built Environment · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsASHRAE 90.1RefrigerantGas compressorCoefficient of performanceEnvironmental scienceHeat pumpMean squared errorSimulationComputer scienceEngineeringStatisticsHeat exchangerMathematicsMeteorologyMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

A new physics-based and modular variable refrigerant flow (VRF) heat pump model aimed toward multi-year simulations is presented. The model allows the simulation of any number of indoor units (IU), outdoor units (OU) and compressors. A parameter-estimation procedure and a control strategy both using available manufacturer data is proposed. The model is validated against data collected from a VRF system that services the first floor of the former ASHRAE Headquarters Building in Atlanta (USA), comprised of 22 IU, 2 OU, and 8 compressors. Results show that the model accurately predicts the total energy consumption over a two-month cooling period, with a relative error, normalized mean bias error, and coefficient of variation of the root mean square error of 1%, 1.6%, and 16.7%, respectively.

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: none
Teacher disagreement score0.584
Threshold uncertainty score0.274

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 head0.218
Teacher spread0.197 · 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