Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

Nivetha Vadamalraj, Kishor Zingre*, Subathra Seshadhri, Pandarasamy Arjunan, Seshadhri Srinivasan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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Abstract

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.
Original languageEnglish
Title of host publicationIndoor Thermal Comfort
EditorsFrancesca Romana D'Ambrosio, Boris Igor Palella
Place of PublicationBasel, Switzerland
PublisherMDPI AG
Pages153-169
Number of pages17
ISBN (Electronic)9783039435289
ISBN (Print)9783039435272
DOIs
Publication statusPublished - Dec 2020

Keywords

  • indoor air quality (IAQ)
  • hybrid ventilation
  • demand controlled ventilation (DCV)
  • internet of things (IoT)
  • soft-sensor
  • convolution neural networks

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