Development, modeling, and optimization of ground source heat pump systems for cold climates: A comprehensive review
Bibliographic record
Abstract
• A comprehensive review of GSHPs for cold climate applications is presented. • A systematic methodology to classify and evaluate various ground heat exchanger designs. • Critical analysis of GSHP performance improvement using solar thermal energy and thermal storage systems. • Review of control and optimization strategies for heat pumps in cold climates. Increasing concerns over anthropogenically-induced climate change are driving the search for alternative, renewable, clean technology to generate energy. Heat pumps are an efficient means of transitioning towards renewable energy sources for space heating, space cooling, and water heating in buildings. The common types of heat pumps are air source heat pumps, which use the ambient air as the energy source and sink, and ground source heat pumps (GSHPs), which use the more stable ground temperatures as the source and sink. In cold climates, the performance of both systems may be compromised, demanding careful design, optimization, and enhancement. GSHPs have the most excellent potential in cold climates where heating loads are significantly higher than cooling loads owing to their use of the more stable ground temperatures. Therefore, this paper provides a comprehensive review of GSHP systems for cold climates, beginning by first introducing the GSHP technology, including a summary of geothermal system classifications and a review of global GSHP systems and their applications. This is followed by an overview of closed-loop systems, including different configurations of ground heat exchangers, and a look at recent innovations in the design of GSHPs. Moreover, studies on the design and performance improvements of open-loop systems are discussed. As a means of improving system performance in cold climates, this paper presents a review of hybrid systems developed by several researchers. Additionally, insights on using GSHP systems for district heating and incorporating thermal storage systems to improve overall system performance are examined. Finally, the control and optimization strategies, as well as economic feasibility and environmental impacts, are reviewed. This study shows the potential to reduce thermal interference radius, thermal imbalance and the length of the heat exchanger when using GSHP systems with latent thermal storage systems and solar recharging. Nonetheless, a need remains for more robust and accurate dynamic prediction models for hybrid heating systems with GSHPs to assess long-term performance and cost-effectiveness.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".