Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions

Feng Xu, Kei Sakurai, Yuki Sato, Yuka Sakai, Shunsuke Sabu, Hiroaki Kanayama, Daisuke Satou and Yasuki Kansha ()
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Feng Xu: Organization for Programs on Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
Kei Sakurai: Organization for Programs on Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
Yuki Sato: Organization for Programs on Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
Yuka Sakai: Organization for Programs on Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
Shunsuke Sabu: Technology and Innovation Center, Daikin Industries, Ltd., 1-1 Nishi-Hitotsuya, Settsu, Osaka 566-8585, Japan
Hiroaki Kanayama: Technology and Innovation Center, Daikin Industries, Ltd., 1-1 Nishi-Hitotsuya, Settsu, Osaka 566-8585, Japan
Daisuke Satou: Technology and Innovation Center, Daikin Industries, Ltd., 1-1 Nishi-Hitotsuya, Settsu, Osaka 566-8585, Japan
Yasuki Kansha: Organization for Programs on Environmental Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan

Energies, 2023, vol. 16, issue 6, 1-13

Abstract: Non-uniform temperature distributions in air-conditioned areas can reduce the energy efficiency of air conditioners and cause uncomfortable thermal sensations for occupants. Furthermore, it is impractical to use physical sensors to measure the local temperature at every position. This study developed a soft-sensing model that integrates the fundamentals of thermodynamics and transport phenomena to predict the temperature at the target position in space. Water experiments were conducted to simulate indoor conditions in an air-conditioning cooling mode. The transient temperatures of various positions were measured for model training and validation. The velocity vectors of water flow were acquired using the particle image velocimetry method. Correlation analysis of various positions was conducted to select the input variable. The soft-sensing model was developed using the multiple linear regression method. The model for the top layer was modified by the correction of dead time. The experimental results showed the temperature inhomogeneity between different layers. The temperature at each target position under two initial temperatures and two flow rates was accurately predicted with a mean absolute error within 0.69 K. Moreover, the temperature under different flow rates can be predicted with one model. Therefore, this soft-sensing model has the potential to be integrated into air-conditioning systems.

Keywords: soft sensor; air conditioning; transient temperature; flow visualization; multiple linear regression; energy saving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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