A study of different modeling approaches for model-based building thermal control
Bibliographic record
Abstract
This thesis presents an assessment of different modeling methodologies for \ndeveloping dynamic thermal models for buildings and discusses the benefits of each for \nmodel-based thermal control in buildings. The modeling section consists of two main parts: \n(1) the development of a detailed dynamic thermal model by means of frequency domain \ntechniques and transfer functions (2) the development of low-order, grey-box, RC \n(resistance-capacitance) thermal network models with parameter optimization. The models \nare verified with experimental data from the Environmental Chamber (EC) test facility at \nConcordia University. Environmental Chamber is an experimental facility that is designed \nto test and calibrate building dynamic thermal models and technologies. The advantages of \neach of the modeling approaches for understanding the thermal behavior of the \nEnvironmental Chamber are discussed. \nA detailed frequency domain model is developed from the application of first principle \nheat transfer equations to study the thermal characteristics of the system. A \ndetailed lumped parameter finite difference model (LPFD) is used as a tool to calculate \nrequired data for the frequency domain model that was not available through experiment. \nLPFD model also provides significant insight into the actual behavior of the chamber under \ntransient conditions. \nThen, the creation of low-order, grey-box, RC circuit model for the Environmental \nChamber is explained, as well as a methodology for optimizing the circuit parameters to \nfind the “effective” resistances and capacitances for a defined objective which is the fit \nbetween measured and simulated air temperature. The challenges encountered while using \nexperimental data to perform optimization for the low-order RC circuits are discussed. \nSuch low-order models that capture the important physics of the problem are best suited to \nreal time MPC in building automation systems in which they can be actually implemented. \nFinally, an analytical frequency domain model is developed for a thermal zone in \nan experimental facility (one of Hydro-Québec's Twin Houses in Shawinigan). The effect \nof different floor coverings on the thermal response of the zone is investigated by means \nof the frequency domain model. Also, using the frequency domain model, the effect of \nincreasing the thermal mass and thermal conductivity of the materials used in the zone on \nthe thermal response of the zone is investigated. The importance of studying the magnitude \nof the zone transfer function for effective thermal storage in the zone in an important certain \nfrequency range is demonstrated. The key advantage of frequency domain modeling for \nevaluating design options without any need to perform simulation is presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".