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Record W2113523011 · doi:10.1109/ccece.2011.6030700

Comparison of apartment building heating control systems

2011· article· en· W2113523011 on OpenAlexaff
Ryan Naughton, Muhammad A. Abbas, Johan Eklund

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsBoiler (water heating)Radiator (engine cooling)Heating systemPID controllerControl systemApartmentTemperature controlController (irrigation)Automotive engineeringWater heatingComputer scienceEngineeringControl engineeringControl theory (sociology)Control (management)Mechanical engineeringWaste managementElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a case study that displays benefits of replacing the control system of an apartment buildings radiator heating system and domestic hot water system with a PID controller. The original control system was inefficient and resulted in unnecessary stress on the boilers as the result of them turning on and off at a greater frequency than required. By comparing collected data for both heating systems boiler operation and system temperatures, it was found that the new PID controller reduces the frequency at which boilers are fired as well as lowering the gas consumption of the heating systems. To allow the controllers to be compared using the same circumstances such as outside temperature and set points a simulation was created. The simulation showed an average of 55% less boiler firings and 20% reduction in gas usage for the radiator system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2011
Admission routes1
Has abstractyes

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