Dealing with corner cases in occupant-centric control: do physics-informed models help?

Occupant-centric control (OCC) of building systems, which improves energy performance and thermal comfort by considering occupants’ presence, preferences, and activity, has drawn substantial research interest over the past decade. However, most existing studies have focused on evaluating overall performance during a relatively long period, overlooking control robustness during transients.

Corner cases in autonomous driving refer to a bug or problem that occurs under unseen, unusual, and perhaps dangerous circumstances. For OCC in buildings, corner cases can be triggered by extreme values or rare patterns of disturbances that are not covered by historical data used to design the controllers. The backbone of many OCC applications is the predictive model, and the root of potential corner cases is that data-driven models sometimes cannot extrapolate well to support decision-making. As the predictive model cannot accurately comprehend the situation or predict what will happen, corner cases could lead to suboptimal or even wrong control actions.

Unlike other data-rich domains, building data typically only covers limited operating conditions and therefore is sparse in the feature space. For example, a model for building control trained with data associated with a normal cooling setpoint of 24°C may be unreliable for such higher setpoints as 27 °C. Considering the difficulty in extensive data collection, methods such as physics-informed neural networks have emerged to enhance the extrapolation capability of data-driven models by incorporating physics-based prior knowledge or exerting physical constraints. Yet, despite the claimed merits such as more data-efficient learning and physics-consistent prediction over long horizons, whether and how physics-informed models can help handle the corner cases and improve control robustness remain unknown.

To address this knowledge gap, we designed a simulation framework that applies OCC in a multi-zone office building. Corner cases were identified using historical data from a real-world campus building. A group of data-driven predictive models will be developed with different levels of physics integration and tested in OCC simulations. Combining actual and synthetic data, the peer models will be comprehensively examined concerning their predictive capability and control performance.

Special attention will be paid to the decision-making process in the corner cases. The experimental results will inform the required level of physics in predictive models to ensure control robustness, leading to two contributions: future development of physics-informed models can be guided in an application-oriented manner, and the actual applications of occupant-centric control can be promoted.