Case Study on Cost Saving and GHG Emission Reduction From Coupling Air Source Heat Pump With Photovoltaic/Thermal Collector
Bibliographic record
Abstract
TRNSYS simulation software was used to modify a validated Air Source Heat Pump (ASHP) model in an Archetype Sustainable House (ASH) in Toronto. In this model, a Building Integrated Photovoltaic-Thermal Collector (BIPV/T) was coupled with ASHP. The PV/T system arrangement was considered as a part of the south-oriented roof of the house. The warm air generated in the BIPV/T collector was considered the source of the heat pump for heat production. The coupling of BIPV/T and ASHP enables a highly efficient heating system in harsh winter conditions. The developed TRNSYS model of the house along with integrated PV/T system with ASHP was simulated for the whole year to predict the hourly outlet air temperature, thermal energy and electricity obtained from the PV/T array. The results from the simulation were used to estimate the saving in energy and cost as well as to predict the electricity related GHG emission reduction potential from the PV panels. Monthly greenhouse gas (GHG) emission credit from PV production based on hourly GHG emission factor was obtained; the results showed that annual GHG emission due to electricity demand by the ASHP was reduced by 225 kg CO2 (19.3%) when the heat pump was integrated with the PV/T array. Also, in this study, the annual electricity cost credit from PV production based on Time-of-Use (TOU) and the reduction in electricity cost of the heat pump when connected with PV/T systems was calculated and compared with the cost of working the heat pump alone. The results show that there is a saving of $500 in annual electricity bills and GHG emission credit of 862.6 kg CO2 from renewable electricity generation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".