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Record W2516716877 · doi:10.1115/1.4034505

A Comparative Study of the Energy-Saving Controllers for Automotive Air-Conditioning/Refrigeration Systems

2016· article· en· W2516716877 on OpenAlex

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.

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2016
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsSimon Fraser UniversityUniversity of Waterloo
Fundersnot available
KeywordsAutomotive industryAir conditioningController (irrigation)Control theory (sociology)RefrigerationControl engineeringPID controllerEnergy (signal processing)Set pointModel predictive controlSet (abstract data type)Computer scienceAutomotive engineeringEngineeringControl (management)Temperature controlMathematicsMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

With the extensive application of air-conditioning/refrigeration (A/C-R) systems in homes, industry, and vehicles, many efforts have been put toward the controller development for A/C-R systems. Therefore, this paper proposes an energy-saving model predictive controller (MPC) via a comparative study of several control approaches that could be applied in automotive A/C-R systems. The on/off controller is first presented and used as a basis to compare with others. The conventional proportional-integral (PI) as well as a set-point controller follows. In the set-point controller, the sliding mode control (SMC) strategies are also employed. Then, the MPC is elaborated upon. Finally, the simulation and experimental results under the same scenario are compared to demonstrate how the advanced MPC can bring more benefits in terms of performance and energy saving (10%) over the conventional controllers.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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