Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914568954970112 |
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| author | Pan, Guanru Reinhardt, Dirk Gros, Sebastien Faulwasser, Timm |
| author_facet | Pan, Guanru Reinhardt, Dirk Gros, Sebastien Faulwasser, Timm |
| contents | This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances, which consolidate all unstructured disturbances into a single quantity that can be estimated from data. Under mild assumptions, the resulting stochastic predictor is causal and distributionally consistent, enabling efficient uncertainty quantification through polynomial chaos expansions and higher-order Chebyshev inequalities. The proposed method is validated using experimental data from a smart home in Norway. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15851 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study Pan, Guanru Reinhardt, Dirk Gros, Sebastien Faulwasser, Timm Systems and Control This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances, which consolidate all unstructured disturbances into a single quantity that can be estimated from data. Under mild assumptions, the resulting stochastic predictor is causal and distributionally consistent, enabling efficient uncertainty quantification through polynomial chaos expansions and higher-order Chebyshev inequalities. The proposed method is validated using experimental data from a smart home in Norway. |
| title | Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.15851 |