Virtual Sensing of Temperatures in Indoor Environments: A Case Study

Real-time measurement of temperatures in indoor environments is important for several reasons, among which we mention the maintenance of comfort levels, the satisfaction of legal requirements, and the energy efficiency. However, placing a sufficient number of sensors at the required locations to guarantee a uniform monitoring of the temperature in a given premise may be difficult, with the result that typically just one or a few sensors are deployed. This is the case, for instance, with thermostats in buildings. Virtual sensing is a technique by which values from physical sensors are replaced by those obtained from virtual ones, which take readings from real sensors and calculate their outputs by means of some process models. In this paper, we consider the case study of temperature monitoring in an open office at Silicon Austria Labs, in Villach (Austria). We perform a comprehensive evaluation of various techniques for the prediction of temperatures recorded by physical sensors on the basis of other sensors, ranging from simple baseline methodologies to more complex classical machine learning and deep learning approaches that allow one to take into account temporal and spatio-temporal relationships in the data. The outcome is that, in this context, it is possible to reach a satisfactory prediction performance by using relatively simple models.