On the proper integration of pre-heating tanks in ground source heat pump systems combined with photovoltaic-thermal panels
Bibliographic record
Abstract
• Pre-heating tanks are investigated to improve the system efficiency. • Pre-heating tank performance depends on the initial borehole field size. • Pre-heating tank is more beneficial for buildings with higher insulation levels. • Pre-heating tanks boost energy efficiency and cut costs in PVT + GSHP systems. Ground Source Heat Pump (GSHP) systems are an energy-efficient solution for meeting heating demands in buildings. However, in cold climates with heating-dominated loads, the net extraction of heat from the ground causes a thermal imbalance, reducing ground temperature and the seasonal performance factor (SPF) of the heat pump over time. Photovoltaic-thermal (PVT) panels can help regenerate ground temperature, improving energy efficiency and potentially reducing the required borehole field size. While the scientific literature has recently clarified the design and potential of GSHP + PVT, limited research has been done on the use of pre-heating tanks to improve the system performance. This study evaluates three GSHP + PVT system layouts using TRNSYS simulations for a multi-family house in Oslo over a 50-year period. The results show that integrating a pre-heating tank on the condenser side increases the 50-year-averaged system SPF 4 from 3.85 (baseline) to 4.11 (+6.8 %), demonstrating enhanced energy efficiency. However, the SPF 4 reduction over time is larger with the pre-heating tank (−7.47 %) due to reduced ground regeneration. In contrast, placing the pre-heating tank on the evaporator side yields a moderate SPF 4 increase to 3.95 (+2.6 %). Additionally, the optimal configurations lead to a 3.6 % reduction in electricity import from the grid, while the impact on PVT electricity generation remains minimal. These findings highlight that pre-heating tanks can effectively enhance GSHP + PVT efficiency, but their placement must be carefully considered to balance short-term efficiency gains with long-term 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".