Error correction method for heat flux and a new algorithm employed in inverting wall thermal resistance using an artificial neural network: Based on IN-SITU heat flux measurements

Bin Xu, Yuan-xia Cheng, Xing-ni Chen, Xing Xie, Jie Ji and Dong-sheng Jiao

Energy, 2023, vol. 282, issue C

Abstract: The heat-flux meter is affected by various uncertain factors in practical applications, which brings some difficulties to scholars in postprocessing data. Therefore, a modified method for calculating the heat flux of the building surface is proposed. It can make the simulated heat flux value consistent with the experimental heat flux value, thus verifying the accuracy of the model with the heat flux value. On this basis, through artificial neural network (ANN) training, the heat conduction flux inside the wall is predicted by using the measured heat flux and temperature of inner surfaces. The results show that the root mean square errors obtained by the heat flux error correction method are all less than 1.12 W·m−2. The ANN fitting coefficients are also greater than 0.9. The error correction method proposed in this work comprehensively considers the influence of solar radiation and the total heat transfer coefficient and is also applicable to passive buildings and buildings with PCM in the envelope. The well-performing network built by ANN can predict the thermal conductivity heat flux inside the wall more accurately, providing a new method to measure or check whether the thermal resistance of the wall is qualified in the field.

Keywords: Heat flux meter; Experimental verification; Thermal resistance of wall; Artificial neural network; Surface heat flux (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223022909

DOI: 10.1016/j.energy.2023.128896

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