Event-Triggered Multi-Kernel Learning-Based Stochastic MPC With Applications in Building Climate Control
Bibliographic record
Abstract
For solving the problem of building climate system uncertainty affected by spatio-temporal variables, an event-triggered multi-kernel learning-based stochastic model predictive control (EMSMPC) method is developed. Compared to the existing stochastic model predictive control (SMPC) methods, the developed method does not require the uncertainty to satisfy strict distributional conditions and can effectively handle the spatio-temporal coupling effects within the uncertainty. Firstly, the spatio-temporal uncertainty is learned via multi-kernel Gaussian process regression. The learning results are employed for constructing the cost function and designing the chance constraint tightening set, thereby ensuring that the chance constraints are satisfied while maintaining the robustness of the controlled system. Then, an event-triggering mechanism is introduced to reduce the frequency of solving optimal control problem (OCP) and online learning, further reducing the energy consumption of the controlled system. Moreover, the feasibility and closed-loop stability of stochastic predictive control method based on multi-kernel learning are critically analyzed. Finally, the effectiveness of the developed method is verified through simulation and experimentation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".