Soil parameter inversion method and application of borehole thermal energy storage system based on DST model

WANG Xiru; FENG Guohui; HUANG Kailiang; DAI Xiaoxin; LIU Qiaoyu; LU Weidong

doi:10.19799/j.cnki.2095-4239.2025.1167

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Soil parameter inversion method and application of borehole thermal energy storage system based on DST model

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-07-13

- Soil parameter inversion method and application of borehole thermal energy storage system based on DST model
- Energy Storage Science and Technology Vol. 15, Issue 6, Pages: 2408-2417(2026)
- 作者机构：
  
  沈阳建筑大学市政与环境工程学院，辽宁沈阳 110168
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.1167
  CLC： TK 529
- Received：29 December 2025，
  
  Revised：2026-01-21，
  
  Published：28 June 2026
- 稿件说明：
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王茜如, 冯国会, 黄凯良, 等. 基于DST模型的地埋管蓄热系统土壤参数反演方法及应用[J]. 储能科学与技术, 2026, 15(6): 2408-2417.

WANG Xiru, FENG Guohui, HUANG Kailiang, et al. Soil parameter inversion method and application of borehole thermal energy storage system based on DST model[J]. Energy Storage Science and Technology, 2026, 15(6): 2408-2417.
王茜如, 冯国会, 黄凯良, 等. 基于DST模型的地埋管蓄热系统土壤参数反演方法及应用[J]. 储能科学与技术, 2026, 15(6): 2408-2417. DOI： 10.19799/j.cnki.2095-4239.2025.1167.

WANG Xiru, FENG Guohui, HUANG Kailiang, et al. Soil parameter inversion method and application of borehole thermal energy storage system based on DST model[J]. Energy Storage Science and Technology, 2026, 15(6): 2408-2417. DOI： 10.19799/j.cnki.2095-4239.2025.1167.

摘要

准确获取土壤热物性参数是地埋管蓄热（BTES）系统进行可靠设计与优化的基础。为解决传统热响应测试（TRT）要求输入恒定、无法准确估计体积热容等问题，提出了一种基于管道地面蓄热模型（DST模型）和实测运行数据的BTES蓄热系统实际性能反演方法，使用基于惯性权值法的粒子群优化算法（PSOIWM），将归一化综合均方根误差（RMSE）作为优化目标，反演土壤初始温度、土壤热导率和土壤体积热容。运用该反演方法，日均出水温度的预测误差从1.28%降低至0.39%；土壤平均温度的预测误差从5.16%显著降低至0.27%。综合RMSE从2.00℃降至0.80℃。对验证期的独立评估进一步证实了反演模型的优越性。相较于初始参数，反演参数预测的出水温度平均相对误差降低了54.2%，土壤平均温度的相对误差全程波动小于0.5%，平均相对误差为0.24%。反演参数预测的综合RMSE为0.58℃，其中RMSE

fluid

为0.25℃，RMSE

soil

为0.05℃，预测精度提升了71%。与采用TRT获得的初始参数工况相比，优化后的反演参数使模型对系统实际性能的预测精度显著提升。本研究为利用实测数据精准反演土壤参数并有效预测系统实际性能提供了一种高效可靠的方法。

Abstract

Borehole thermal energy storage (BTES) technology has been identified as a key solution to the mismatch between waste heat generation and utilization. Accurate determin

ation of the thermal physical parameters of soil is paramount for the reliable design and optimization of the BTES system. To address the limitations of the conventional thermal response test (TRT)

such as the requirement for constant input and the inability to accurately estimate the volumetric heat capacity

a method for inverting the actual performance of the BTES system based on the duct ground heat storage model and measured operation data is proposed. The particle swarm optimization algorithm based on the inertia weight method was employed to minimize the normalized comprehensive root mean square error (RMSE) to invert the initial soil temperature

thermal conductivity

and volumetric heat capacity. An experimental platform was established for the cross-seasonal heat storage and heating system of buried pipes. Following a three-month heat storage period

an increase in the average soil temperature was observed. The heat storage body of the buried pipes exhibited an increase from 12.81℃ to 16.09℃. The average daily heat storage capacity was determined to be 344.99 kW·h

and the total heat storage capacity was found to be 32.08 MW·h. The application of the proposed inversion method reduced the prediction error of the average daily effluent temperature from 1.28% to 0.39%. The prediction error of the average soil temperature decreased significantly from 5.16% to 0.27%. Consequently

the comprehensive RMSE decreased from 2.00℃ to 0.80℃. An independent assessment of the validation period further confirmed the superiority of the proposed inversion model. Compared with the initial parameters

the average relative error of the discharge temperature predicted by the inverted parameters decreased by 54.2%. The relative error of the average soil temperature fluctuated below 0.5% throughout the process

with an average relative error of 0.24%. The comprehensive RMSE for the inverted parameter predictions was 0.58℃

comprising RMSE

fluid

and RMSE

soil

of 0.25℃ and 0.05℃

respectively

representing a

71% improvement in prediction accuracy. A comparison of the initial parameter conditions obtained using the TRT with the optimized inverted parameters reveals a significant enhancement in the predictive accuracy of the model for the actual system performance. This study proposes an efficient and reliable method for precisely inverting soil parameters using measured data and effectively predicting the actual system performance.

关键词

Keywords

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