Linear model predictive control for the reduction of auxiliary electric heating in residential self-assisted ground-source heat pump systems
Bibliographic record
Abstract
This article presents a linear model predictive control strategy for the operation of a “self-assisted” ground-source heat pump (GSHP) to reduce auxiliary electric heating in residential applications equipped with undersized boreholes. The self-assisted configuration uses an electric heating element at the heat-pump outlet to inject heat into the bore field when approaching peak power demand. A linear control-oriented model is proposed to account for both the source-side and load-side GSHP dynamics. The ground heat transfer is predicted using the bore field’s ground-to-fluid thermal response factor, thus allowing for any bore field configuration while accounting for thermal capacity effects. Real historic ambient temperature forecasts and their corresponding historic recorded ambient temperatures from Montreal are used in this article. The coefficient of performance (COP) nonlinearity is circumvented with an iterative approach. A Kalman filter is used to dynamically adjust the bias on the predicted returning fluid temperature. On a borehole undersized by 15%, the control strategy reduces auxiliary electric heating by 96% over 20 years at the cost of a 5.53% increase in total energy consumption. Due to the occasional simultaneous heat injection and auxiliary heating, the yearly peak power demand is increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".