Modeling of an Indirect Solar Assisted Heat Pump System for a High Performance Residential House
Bibliographic record
Abstract
The combination of solar thermal and heat pump systems as a single solar assisted heat pump (SAHP) system can significantly reduce residential energy consumption in Canada. As a part of Team Ontario's efforts to develop a high performance house for the U.S. Department of Energy's Solar Decathlon 2013 Competition, an integrated mechanical system (IMS) consisting of a SAHP was investigated. The system was designed to provide domestic hot water (DHW), space-heating, space-cooling, and dehumidification. The system included a cold and a hot thermal storage tanks and a heat pump to move energy from the low temperature reservoir to the hot reservoir. Solar thermal collectors supplied heat to the cold storage and operated at a higher efficiency due to the heat pump reducing the temperature of the collector working fluid. The combination of the heat pump and solar thermal collectors allows more heat to be harvested at a lower temperature, and then boosted to a suitable temperature for domestic use via the heat pump. The IMS and the building's energy loads were modeled using the TRNSYS simulation software. A parametric study was conducted to optimize the control, sizing, and configuration of the system. The simulation results suggested that the investigated system can achieve a free energy ratio (FER) of about 0.583 for the high performance house designed for the Ottawa climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".