A Simplified Ground Thermal Response Model for Analyzing Solar-Assisted Ground Source Heat Pump Systems
Bibliographic record
Abstract
Ground source heat pump systems that are installed in areas with heating or cooling dominant seasons, or in buildings with utilization characteristics that lead to a disparity in demand, often encounter challenges related to ground thermal imbalance. This imbalance can lead to long-term ground temperature changes and may cause premature system failure. This paper focuses on combining a ground source heat pump system with a solar thermal array, with the goal of eliminating the effect of ground thermal imbalance, and minimizing system lifetime cost. A thermal mass ground heat transfer model is combined with a time-stepping model to analyze the system for a variety of solar array sizes. The details associated with this modelling technique are presented, and case studies are provided to illustrate the results of the calculations for three different buildings. It is shown that increasing the solar array size can offset ground thermal imbalances, but increasing the array size also results in a larger initial system cost. An economic analysis is then carried out to determine the system lifetime cost as a function of this solar array size, and an optimal array size from an economic perspective was found. The result of the study shows that hybridizing a ground source heat pump system with a solar array produces a viable system from a technical and economic standpoint, can be used to avoid premature system failure, and can reduce system lifetime cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".