An integrated framework of ground source heat pump utilisation for high-performance buildings
Bibliographic record
Abstract
emissions from building operations have increased to their highest level globally, moving away from the Paris Agreement goal of below 2 °C. While geothermal is recognised as a promising renewable source, the lack of an integrated framework guiding investigating ground source heat pumps for building operations, along with the incapability of well-known simulation tools in accurately capturing ground thermal performance, hinders its application. This research aims to unlock ground source heat pumps for building operations through an integrated framework, including an overarching improved U.S. National Renewable Energy Laboratory (NREL) monitoring guideline, a sensor-based monitoring prototype, and a g-function-based simulation approach. This research proposes amendments and improvements to the NREL guideline for monitoring geothermal energy by separating Thermal Energy Net Production from Thermal Energy Gross Production. A state-of-the-art case building located in Melbourne, Australia, housing advanced technologies, including ground source heat pump systems, is used to demonstrate and validate the research framework. A typical winter month in the southern hemisphere, July 2021, is monitored for the ground source heat pump systems designed and used for space heating. The findings reveal that the thermal energy generation during working days in July 2021 is close to the simulation results, with a difference of 2.2% in gross thermal energy production and a difference of 0.92% in inlet temperature. This research develops and validates an integrated approach for evaluating ground source heat pump systems, contributing to the utilisation of geothermal energy for building operations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".