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

Author

Listed:
  • 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
    Technology and Innovation Center, Daikin Industries, Ltd., 1-1 Nishi-Hitotsuya, Settsu, Osaka 566-8585, Japan)

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.

Suggested Citation

  • Feng Xu & Kei Sakurai & Yuki Sato & Yuka Sakai & Shunsuke Sabu & Hiroaki Kanayama & Daisuke Satou & Yasuki Kansha, 2023. "Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions," Energies, MDPI, vol. 16(6), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2870-:d:1102371
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    References listed on IDEAS

    as
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