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Record W3033485401 · doi:10.11575/prism/37333

Modeling of the Water-based Heating System of the Mechanical Engineering Building at the University of Calgary

2019· dissertation· en· W3033485401 on OpenAlexaboutno aff
Ahmed Saeed

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEngineeringArchitectural engineeringMechanical engineeringConstruction engineeringCivil engineeringSystems engineering

Abstract

fetched live from OpenAlex

In this research, the model of the water-based heating system of Mechanical Engineering Building (MEB) is developed. This project is conducted in collaboration with “Office of Sustainability” of University of Calgary (UofC), because one of their goals is to reduce the energy consumption of UofC’s buildings. The water-based heating system has one of the major share in total energy consumption of a building. It highlights the importance of building this model, which can help to understand some important aspects and variables (related to energy consumption) of the water-based heating system. The model has four major component models, namely boiler, Air Handling Unit (AHU), Reheat Coil (RHC) and radiator (RAD). A component model aggregately represents the similar type of equipment in MEB. For example, a single boiler model is used to represent two boilers of the water-based heating system of MEB. The component models of AHU and RHC are based on energy balance equations, and these are gray-box models. However, the models of boiler and RAD are black-box models, because some required data is not available for developing their gray-box models. The empirical data for developing component models is collected through Building Management System (BMS) software, with the help of Office of Sustainability. The model is developed in Simulink. An individual model for each component is developed and then parameters are estimated for each component model. The parameters of gray-box models are estimated in Simulink, whereas the coefficient parameters for black-box models are estimated in MS-Excel. The output of each component model is then compared with the measured data to ascertain the error. The integrated model of the water-based heating system is developed by connecting the component models. Being a pilot project, the worked helped all involved to understand the opportunities available and the difficulties present, to undertake a project related to UofC building heating system. The Simulink model developed can help the facility management of UofC to look into energy consumption of the water-based heating system of MEB.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.109
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.193
Teacher spread0.184 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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