Data-driven modeling techniques for indoor CO2 estimation

© 2017 IEEE. 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.

ISBN:9781509010127

Publication year:2017

BOF-keylabel:yes

IOF-keylabel:yes

Authors from:Higher Education

Accessibility:Closed