Heat Pump Water Heater Control Strategy Optimization for Cold Climates
Bibliographic record
Abstract
This paper presents a study which was conducted to evaluate the performance of a commercially available heat pump water heater (HPWH) with modified controls. The HPWH is first characterized experimentally under a series of different thermal conditions and draw parameters. The test tank contains a 1500 W electric auxiliary heater that provides on demand heat to the top 0.30 m (1 ft) of the tank, and a wrap-around heating coil. An air source heat pump (ASHP), using R-134A as the refrigerant, draws air from, and returns air to the surrounding space and provides heating to the whole tank through the coil. The tank has been tested using Canadian Standards Association draw profiles to characterize performance under different hot water demands. Electricity consumption and thermal flux is measured for each vertical tank section, and various performance metrics are calculated using energy balances. A trnsys model is then calibrated to the experimental data to allow for the flexibility of varying multiple parameters over various climates. Using this calibrated trnsys model, an optimal control strategy and tank setpoints can be determined for use in cold climates. As expected from previous work, there is a decrease in performance of the HP when heating the tank to higher temperatures to facilitate thermal storage, but the benefits from taking advantage of shifting electrical demand (of water heating) to space heating demand can outweigh the loss of performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".