Sensorless air flow control in an HVAC system through deep learning

Junseo Son, Hyogon Kim

Research output: Contribution to journalArticle

Abstract

Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

Original languageEnglish
Article number3293
JournalApplied Sciences (Switzerland)
Volume9
Issue number16
DOIs
Publication statusPublished - 2019 Aug 1

Fingerprint

air conditioning
ventilation
air flow
Flow control
Air conditioning
learning
Ventilation
Heating
heating
sensors
Sensors
Air
intelligence
conditioning
costs
Intelligent buildings
operating costs
Costs
static pressure
Pressure sensors

Keywords

  • Cost reduction
  • Deep learning
  • HVAC
  • Sensor-less
  • Static pressure

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

Sensorless air flow control in an HVAC system through deep learning. / Son, Junseo; Kim, Hyogon.

In: Applied Sciences (Switzerland), Vol. 9, No. 16, 3293, 01.08.2019.

Research output: Contribution to journalArticle

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