Offset-Free Model Predictive Control of a Heat Pump
Bibliographic record
Abstract
This work presents an offset-free model predictive control (OF-MPC) design for energy-efficient control of a heat pump. A model developed from a combination of first-principles and empirical components with parameters estimated using real heat pump data is used as a test bed for the implementation of the MPC design. Open-loop system dynamics are examined first to design and implement the appropriate control structure. Next, a linear model is identified using appropriate step test simulations on the detailed heat pump model. Subsequently, a model predictive controller formulation, which eliminates the tradeoff between tracking and energy objectives, is designed. The MPC includes an augmented model (including disturbance states) and an associated Luenberger observer to estimate the disturbance (plant-model mismatch at steady state). Simulation results subject to realistic disturbances and measurement noise demonstrate that energy savings can be achieved (anywhere between 1.7% and 1.9%), while preserving a safer operating window (a 43% improvement, with respect to a safe operation measure) with the proposed OF-MPC design that achieves tighter tracking regulation (improvement of 40% and above), compared to a traditional control approach.
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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 | 0.000 | 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".