Characterizing the Effect of an Off-Peak Ground Pre-1 Cool Control Strategy 2 on Hybrid Ground Source Heat Pump Systems
Bibliographic record
Abstract
Geo-exchange systems are a sustainable alternative to conventional space conditioning systems due to their high operating efficiency, resulting in reduced energy consumption and greenhouse gas emissions. However, geo-exchange’s ability to penetrate the market has been throttled by large capital investments, resulting in undesirable payback periods. Optimized hybrid ground source heat pump systems (HGSHP) systems have been introduced as a remedy to overcome the current economic hurtles associated to the installation of geo-exchange systems. In both the literature, as well as in practice, there still remains potential for increased economic feasibility of this technology through integration of intelligent operational strategies. This paper presents a novel control methodology referred to as an off-peak ground pre-cool strategy, employing a time-of-use conscious operating logic which artificially pre-condition the system’s bore-field. Reducing peak power consumption is achieved by creating improved thermal characteristics during mid-peak/peak time-of-use operating brackets. A comprehensive numerical model was developed to characterize the operation of HGSHP systems for three real case studies. The mode implemented a base case set-point control scheme, used as a reference to assess the operational benefit of the proposed off-peak ground pre-cool control strategy. The preliminary analyses indicated operational cost savings of up to 16.4%, under specific pre-cool scheduling. The strategy indicated reductions in both carbon emission and peak power consumption of up t 19 15.0% and 58.5%, respectively. In all cases increasing cooling supplied by the hybrid geo 20 exchange system was indicated, with a maximum observed capacity increase of 43.7%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".