Simulation of Model-based Predictive Control Applied to a Solar-assisted Cold Climate Heat Pump System
Bibliographic record
Abstract
This paper presents a simulation study of a model-based predictive control (MPC) strategy applied to a BIPV/T-assisted cold climate heat pump. When innovative technologies are brought into play, reaching their full potential depends on proper operation and control. For instance, the coordination of renewable energy systems with a highly variable output, thermal energy storage devices and fluctuating building load conditions can significantly benefit from advanced control and operating strategies. MPC strategies, combined with adequate energy storage capabilities, can make a critical positive impact in the implementation of innovative solutions. The mechanical system under study, designed during the early design phase of a 2000-m2 net-zero energy building, consists of a BIPV/T roof with an electrical output of nearly 52 kW and a total area of 320 m2, a set of two air-source cold-climate heat pumps (PUHY-HP96, Mitsubishi) and a 20-m3 energy storage tank. Frost and de-frost cycles of the heat pump are considered in this study, as well as the power used by the fans in the BIPV/T system. The performance of the system is compared with a more conventional ground-source system. The dynamic simulation of the building and its systems was implemented in MATLAB/Simulink using relatively simple, low-order models with inputs from expected occupancy levels and typical meteorological year (TMY) weather data. Models for the BIPV/T system and the TES tank were also developed. A look-up table model was used for the simulation of the heat pumps. Simulation results shed light on design decisions concerning the building systems and facilitate the development of control strategies to achieve a smooth and successful operation. Model-based predictive control algorithms were used to select the sequence of optimal states of charge for the TES tank as a function of expected weather conditions and occupancy patterns.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".