Thermal Performance Analysis of an Air Source Heat Pump Coupled With a Solar Air Collector for Domestic Water Heating in a Cold Climate
Bibliographic record
Abstract
Abstract Reducing building-related CO2 emissions requires decreasing energy consumption and/or adopting cleaner, more efficient technologies. Air source heat pumps (ASHPs) are increasingly favored for building heating, especially in mild climates. When powered by clean and renewable energy sources, they offer significant potential for emission reduction. However, cold climates present unique challenges for these conventional heating systems, prompting interest in integrating renewable energy sources such as solar energy and energy storage with ASHPs to maintain consistent performance and lower their energy use during the coldest days. This study investigates the performance of an air source heat pump integrated with a solar air collector for domestic water heating in a cold climate. The study aims to quantitatively evaluate the contribution of the solar air collector to the overall reduction in energy consumption, enabling a thorough assessment of the integrated system’s efficiency and operational performance. The coupled air collector – ASHP – TES tank model was developed using a transient system simulation tool (TRNSYS). The impact of collector orientation, with tilt angles between 30 and 70°, collector surface areas between 8 and 16 m2 and mass flow rates ranging from 0.25 to 1 kg/s, which are considered suitable for a 2-ton heat pump, were systematically examined. Results show that the optimal tilt angle for maximizing useful annual energy gain is 51°, aligned with Calgary’s latitude, surpassing alternative tilt angles (30, 40, 60, and 70°). Furthermore, the integration of the solar air collector improves the annual average coefficient of performance (COP) by 16 % and reduces the annual average compressor work by 8%. This comprehensive analysis highlights the potential of integrating solar energy with ASHPs to enhance hot water efficiency and reduce energy consumption in cold climates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".