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Open AccessArticle

Sensorless Air Flow Control in an HVAC System through Deep Learning

1,*,† and 2,‡
1
A&B Center, CTO, LG Electronics, Seoul 08592, Korea
2
Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Gasan R&D Campus, LG Electronics, 51, Gasan digital 1-ro, Geumcheon-gu, Seoul 08592, Korea.
Department of Computer Science and Engineering, Korea University, Anam-Dong, Sungbuk-Gu, Seoul 02841, Korea.
Appl. Sci. 2019, 9(16), 3293; https://doi.org/10.3390/app9163293
Received: 16 June 2019 / Revised: 31 July 2019 / Accepted: 9 August 2019 / Published: 11 August 2019
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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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). View Full-Text
Keywords: HVAC; sensor-less; deep learning; cost reduction; static pressure HVAC; sensor-less; deep learning; cost reduction; static pressure
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Son, J.; Kim, H. Sensorless Air Flow Control in an HVAC System through Deep Learning. Appl. Sci. 2019, 9, 3293.

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