Journal
ENERGIES
Volume 16, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/en16062870
Keywords
soft sensor; air conditioning; transient temperature; flow visualization; multiple linear regression; energy saving
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Non-uniform temperature distributions in air-conditioned areas can reduce energy efficiency and cause discomfort. This study developed a soft-sensing model based on thermodynamics and transport phenomena to predict temperature in space. Water experiments were conducted to simulate indoor conditions and measure transient temperatures for model training. The model, developed using multiple linear regression, accurately predicted target temperatures with a mean absolute error within 0.69 K. This soft-sensing model has the potential to be integrated into air-conditioning systems.
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.
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