Digital Twins for Building Pseudo-Measurements

Building control architectures are strongly limited by the systematic lack of measurements at user-relevant locations. This article proposes a digital twin (DT) architecture grounded in correlated Gaussian processes (Corr-GPs) that provide information in the form of pseudo-measurements. Tested with thermal and CO2 measurements collected from the field, close-to-person pseudo-measurements are provided based on the continuous input of remotely located measurement signals. In particular, detailed short-term as well as long-term results are provided for both temperature and CO2 DTs. We show that the proposed approach is trainable on only a few days of measurements. This property makes the proposed approach especially useful in field applications, where alternative algorithms, such as, for example, neural network architectures, are not capable of dealing with small amounts of data. We demonstrate how to adjust the proposed approach to provide temperature and CO2 DTs for the generation of pseudo-measurements. In the given framework, we show how to utilize the proposed DT to couple multiple reference sensors to provide close-to-person pseudo-measurements. By extending the Corr-GP approach to a nonzero prior mean formulation, we show how to reduce the included information by the reference sensors. More precisely, the extended approach can be defined as a DT with only a single reference sensor. This enables a reliable long-term application by avoiding the need for retraining caused by changing seasonalities within the signal characteristics. That is, we show that the DT trained in summer can be operated in winter.