Data-driven modeling techniques for indoor CO2 estimation

Bob Vergauwen, Oscar Mauricio Agudelo, Raj Thilak Rajan, Frank Pasveer, Bart De Moor

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIEEE SENSORS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages3
ISBN (Electronic)9781509010127
Publication statusPublished - 2017
Externally publishedYes
Event16th IEEE SENSORS Conference, ICSENS 2017 - Glasgow, United Kingdom
Duration: 30 Oct 20171 Nov 2017

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference16th IEEE SENSORS Conference, ICSENS 2017
Country/TerritoryUnited Kingdom


  • air quality
  • CO-estimation
  • LS-SVM
  • non-linear modeling
  • WSN


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