Sensorless air flow control in an HVAC system through deep learning

Junseo Son, Hyogon Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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)
Issue number16
Publication statusPublished - 2019 Aug 1

Bibliographical note

Funding Information:
Funding: This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20188550000410).

Publisher Copyright:
© 2019 by the authors.


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

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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