Rapid Temperature Prediction Model for Large-Scale Seasonal Borehole Thermal Energy Storage Unit

Donglin Zhao, Mengying Cui, Shuchuan Yang, Xiao Li, Junqing Huo and Yonggao Yin ()
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Donglin Zhao: School of Energy and Environment, Southeast University, Nanjing 210096, China
Mengying Cui: School of Energy and Environment, Southeast University, Nanjing 210096, China
Shuchuan Yang: School of Energy and Environment, Southeast University, Nanjing 210096, China
Xiao Li: Hebei Zhuopai New Energy Resources Development Co., Ltd., Shijiazhuang 051230, China
Junqing Huo: Hebei Zhuopai New Energy Resources Development Co., Ltd., Shijiazhuang 051230, China
Yonggao Yin: School of Energy and Environment, Southeast University, Nanjing 210096, China

Energies, 2025, vol. 18, issue 19, 1-23

Abstract: The temperature of the thermal energy storage unit is a critical parameter for the stable operation of seasonal borehole thermal energy storage (BTES) systems. However, existing temperature prediction models predominantly focus on estimating single-point temperatures or borehole wall temperatures, while lacking effective methods for calculating the average temperature of the storage unit. This limitation hinders accurate assessment of the thermal charging and discharging states. Furthermore, some models involve complex computations and exhibit low operational efficiency, failing to meet the practical engineering demands for rapid prediction and response. To address these challenges, this study first develops a thermal response model for the average temperature of the storage unit based on the finite line source theory and further proposes a simplified engineering algorithm for predicting the storage unit temperature. Subsequently, two-dimensional discrete convolution and Fast Fourier Transform (FFT) techniques are introduced to accelerate the solution of the storage unit temperature distribution. Finally, the model’s accuracy is validated against practical engineering cases. The results indicate that the single-point temperature engineering algorithm yields a maximum relative error of only 0.3%, while the average temperature exhibits a maximum relative error of 1.2%. After employing FFT, the computation time of both single-point and average temperature engineering algorithms over a 10-year simulation period is reduced by more than 90%. When using two-dimensional discrete convolution to calculate the temperature distribution of the storage unit, expanding the input layer from 200 × 200 to 400 × 400 and the convolution kernel from 25 × 25 to 51 × 51 reduces the time required for temperature superposition calculations to approximately 0.14–0.82% of the original time. This substantial improvement in computational efficiency is achieved without compromising accuracy.

Keywords: seasonal borehole thermal energy storage; temperature modeling of thermal energy storage unit; engineering algorithm; two-dimensional discrete convolution; Fast Fourier Transform (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: 2025
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