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Record W2800170713 · doi:10.1155/2018/4303580

Modeling, Analysis, and State Feedback Control Design of a Multizone HVAC System

2018· article· en· W2800170713 on OpenAlexafffund
Almahdi Abdo-Allah, M. Tariq Iqbal, Kevin Pope

Bibliographic record

VenueJournal of Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Bureau for International Education
KeywordsHVACState-space representationMATLABState spaceMIMOControl engineeringController (irrigation)System identificationToolboxControl theory (sociology)Air conditioningControl systemEngineeringEnergy consumptionComputer scienceSimulationData modelingControl (management)Mechanical engineeringElectronic engineering

Abstract

fetched live from OpenAlex

A HVAC system is modeled by applying a state space MIMO (multi-input/multioutput) system method for control system design and analysis. Thermal models are developed using the simulation program IDA Indoor Climate and Energy. The building has four floors in total, with separate air-handling units (AHUs) on each floor. The system’s eight main input data are hot water and the energy usage for each AHU, while the eight main outputs are return airflow temperature and CO 2 levels for AHUs. The factors of wind direction and velocity are also applied as disturbances. By comparing usage data on simulated power consumption versus measured data for the three months of October, November, and December 2016, good agreement was achieved with simulated data. The main aim is to develop a state feedback controller and then apply it toward optimal functionality of a control system. After utilizing the MATLAB identification toolbox, a MIMO system-based state space model is developed.

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 score0.884
Threshold uncertainty score0.270

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 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".

Quick stats

Citations10
Published2018
Admission routes2
Has abstractyes

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