Heat Pump Capacity Effects on Peak Electricity Consumption and Total Length of Self- and Solar-assisted Shallow Ground Heat Exchanger Networks
Bibliographic record
Abstract
A new "self-assisted" Ground Source Heat Pump (GSHP) system configuration is proposed to address the relatively high peak electricity demand of undersized GSHP systems equiped with auxiliary electric heater.In this configuration, ground heat exchangers (GHE) have two independent circuits: the first circuit is used to inject the extra heat produced by the heat pump into the ground during off-peak operations, while the second circuit is used to extract heat in the winter and reject heat in the summer for space heating and cooling, respectively.This configuration is compared against a "solar-assisted" configuration and a conventional single U-tube configuration.An analytical model for shallow GHE networks is used to evaluate the effects of the heat pump nominal capacity and the borehole total length on the total electricity consumption and peak electricity demand of the three configurations.Results show that the self-assisted configuration reduces the peak electricity demand by 47%, in a case with a 29% undersized GHE network and a 16% undersized heat pump nominal capacity, while it increases the total energy consumption by 4.1%.Using a solar-assisted configuration for the same sizing parameters reduces the peak electricity demand by only 6.3% and the total energy consumption by 3.8%.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".