A Multiple Load Aggregation Algorithm for Annual Hourly Simulations of GCHP Systems
Bibliographic record
Abstract
This article presents a technique to aggregate heating/cooling loads when using the cylindrical heat source method (CHS) to perform annual hourly energy simulations of ground-coupled heat pump (GCHP) systems. The technique, referred to as “multiple load aggregation algorithm” (or MLAA), uses two major thermal history periods, referred to as “past” and “immediate.” In addition, the MLAA accounts for thermal interference among boreholes by numerically solving the two-dimensional temperature field in the borefield. Results of a comparison between the MLAA and the duct storage (DST) model are presented. Several cases are examined with two different borefields and several load profiles. Results obtained for one- and ten-year simulations show that the MLAA is in very good agreement with the DST model. In the worst case, the maximum difference in fluid temperature is of the order of 2 K (3.6°F). This level of precision is more than adequate to perform accurate hourly simulations of GCHP systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".