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Record W2048178471 · doi:10.1115/dscc2013-3897

A Model-Based Predictive Control Approach for a Building Cooling System With Ice Storage

2013· article· en· W2048178471 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsModel predictive controlThermal energy storageChillerComputer scienceDuty cycleConstraint (computer-aided design)ElectricityWater coolingControl theory (sociology)EngineeringControl (management)VoltageMechanical engineering

Abstract

fetched live from OpenAlex

This paper presents a model predictive control (MPC) algorithm designed for the cooling system of a small commercial building under a time-dependent electricity price profile. The proposed approach includes a problem formulation in terms of cooling power, a variable-length prediction horizon and the consideration of the equipment duty cycle as a constraint in the optimization algorithm. The cooling system is equipped with an ice bank for thermal energy storage. A simple linear building thermal model is used to calculate the required amount of cooling power to maintain thermal comfort. The MPC algorithm uses this information to find the optimal operating points for the chiller and the ice bank to minimize the electric energy cost. The results of the MPC algorithm are compared against those of the reactive rule-based control algorithm currently in use in the building.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.680

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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