A virtual sensor network for pressure distribution inside a multi-zone building based on spatial adjacency relationships and multivariate adaptive regression spline

Abstract

Airflows within multi-zone buildings cause variations in environmental parameters both vertically and horizontally. Although it is difficult to monitor the airflows through existing sensors, pressure monitoring allows the identification of airflows, since airflows across zones are induced by pressure differences. This study proposes a data-driven method for establishing a virtual pressure sensor network within a multi-zone building. Three scenarios with various sample sizes are applied to evaluate their impacts on the analyzed results, and scenario 3 is identified as the most suitable database for creating a virtual sensor network. The single-sided or multi-sided spatial adjacency relationships are determined for each zone, and the MARS model is then applied to develop the relationships among the variables, with key physical sensors designated by the frequency analysis of independent variables. The final sensor network comprises four key physical sensors, thirteen direct virtual sensors, and five indirect virtual sensors. All virtual sensors can achieve excellent predicted results with high R2 values and low MAE and RMSE, with mean values of 0.997 Pa and 1.269 Pa, respectively. Notably, the difference in accuracy between direct and indirect virtual sensors is influenced by spatial adjacency relationships. This study demonstrates a reliable virtual sensor network for predicting pressure variations and provides a reference for the identification of airflows and the efficient adjustment of HVAC systems.