A model-based continuous commissioning method for an efficient integration of ground source heat pumps in the building ecosystem
Bibliographic record
Abstract
This paper introduces a model-based continuous commissioning (MBCCx) methodology specifically designed for the identification of control-related performance gaps within heating, ventilation and air conditioning (HVAC) systems equipped with ground-source heat pumps (GSHPs). While conventional continuous commissioning (CCx) is effective in detecting energy performance gaps, MBCCx goes further by using a system model as a reference to pinpoint operational inefficiencies and control faults arising from subsystem integration. The core of the proposed methodology lies in a calibrated physics-based model that represents the system performance as intended during the design phase. A key advantage is its applicability early in a building’s operational phase , when data is limited, unlike data-driven methods that rely on extensive historical datasets. This enables the identification of energy-saving opportunities before the system reaches a stable operational state. To address the limitations of prior studies that often focus solely on individual GSHP component performance, this work pioneers the application of MBCCx to whole buildings equipped with GSHPs. The proposed approach employs a detailed 3D building model and component-level HVAC modeling to predict parameters such as room temperatures, heat pump power, and ground heat exchanger temperatures under normal conditions. Significant deviations between monitored values and model predictions serve as indicators of underperforming components or control sequence anomalies. The anomaly detection accuracy is then improved by merging HVAC system and GSHP performance indicators. The methodology is demonstrated through a case study of a recently retrofitted elementary school in Québec, Canada, equipped with five standing column wells as ground heat exchangers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".