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Building Energy and IAQ improvement by Coupled Model

2019· article· en· W2981691112 on OpenAlexaff
Seyedmohammadreza Heibati, Wahid Maref, Hamed H. Saber

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsIndoor air qualityVentilation (architecture)AirflowInfiltration (HVAC)Efficient energy useEnergy (signal processing)SimulationEngineeringComputer scienceEnvironmental scienceEnvironmental engineeringMechanical engineeringMeteorologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract The building performances are related to Energy Efficiency and Indoor Air Quality (IAQ). Modeling is one of the best accurate tools for measuring the building performance. Nowadays, Energy Efficiency and IAQ are modeled individually for buildings. Improvement strategies in both areas are analyzed separately. The fundamental problem in Energy and IAQ modeling is related to interaction to each other. This problem makes the modeling results unrealistic to the building performance solutions. To avoid this problem, in this current research, Energy and IAQ models are coupled simultaneously as a new co-simulation method. EnergyPlus and CONTAM are used as Energy and IAQ models, respectively. With the co-simulation method, these two models are coupled together. The method is based on the exchange of control variables between both models dynamically and simultaneously. As a result, the exchanges of temperature and air flow variables are corrected. The verification of the new model is based on the comparison of the simulation and analytical results of temperature and air flow variables. In the next step, this new coupling-co-simulation method for a townhouse building is done in two cases: a leaky and a tight building envelopes. Both cases are compared in two types of ventilation systems: infiltration only, and exhaust only. At this point, the simulated air change rates, gas energy use, and particles concentrations are compared for each case. Finally, the necessity of the accuracy of this new method is concluded.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.667

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 communication0.0000.001
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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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 designBench or experimental
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

Citations3
Published2019
Admission routes1
Has abstractyes

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