Ground Source Heat Pump Modeling and Aggregation for Services Provision in Electricity Markets
Bibliographic record
Abstract
Thermal load systems may be capable of participating in energy markets through load aggregators to optimize its load demand, but it could also provide other ancillary services, such as load shifting, on-peak load demand reduction, and provision of Demand Response (DR) services. A thermal load aggregation approach to minimize the aggregator's energy procurement cost is proposed in this paper, together with a mathematical model based on the thermal load, particularly Ground Source Heat Pump (GSHP), characteristics to optimize the electricity usage by end-users, while considering household thermal comfort. Simulations of an aggregator's optimal heating load dispatch with a conventional Heating Ventilation and Air Conditioning (HVAC) and proposed GSHP alternative are presented, demonstrating the effectiveness of the proposed twostage strategy for optimal aggregator load dispatch of HVAC and GSHP systems, and the advantages of GSHP compared to HVAC.
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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 | 0.000 | 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".