Event-Triggered Multi-Kernel Learning-Based Stochastic MPC With Applications in Building Climate Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it