High resolution time-series modeling of domestic hot water heating systems
Bibliographic record
Abstract
This thesis evaluates domestic water heating systems in conjunction with energy saving technologies such as solar water heating, drain water heat recovery, and heat pump water heating. Five dynamic models are developed using Matlab Simulink® with a time-step of one minute. Using minute resolution hot water flow, hourly solar radiation data and ambient temperature, the performance of various configurations are assessed when operating in Victoria, Kamloops, and Williams Lake, B.C. Twelve different demand profiles on a summer day and winter day are simulated. Some specific metrics, such as conventional energy consumption, system energy factor, and equivalent CO2 emissions are used as the basis of evaluating the system efficiency. Results indicate the potential improvements in system performance over a conventional domestic water heating system in lower conventional energy consumption and lower CO2 emissions when applying any one of the three energy saving technologies mentioned above. For example, on a representative summer day (Day 228) in Victoria with a load profile of a low-use two-person family on a weekday, the system‟s energy factor can be improved from 0.50 to up to 2.84, and the corresponding conventional energy consumption and the CO2 emissions decrease from 9.86 kwh to 1.67 kwh, and 1.77 kg/day to 0.06 kg/day, respectively depending on which energy saving technology is applied. The modeling tool developed in this research can be used to guide the design of domestic water heating systems with various system configurations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".