Data-driven modeling techniques for indoor CO₂ estimation

Abstract

This paper presents the results of using the Least-Squares Support Vector Machines (LS-SVMs) framework for estimating CO₂ levels at the Holst Center building in the Netherlands. Within the IoT framework, a Wireless Sensor Network (WSN) consisting of seven sensors is currently deployed at the third floor of the building. Each sensor node provides measures of temperature, relative humidity and CO₂ levels, and transmits the readings to a consumer accessible cloud. Given that CO₂ has a big impact on people comfort and productivity, its monitoring and control has become a common practice in recent years. In this work we provide a way to estimate the CO₂ concentration when a CO₂ sensor is not trustworthy (e.g., due to maintenance or a malfunction), by using nonlinear models built from historical sensor data. Results showed that the model structures proposed in this work provided better CO₂ estimates than those given by conventional linear autoregressive (AR) and autoregressive exogenous (ARX) models.