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Multivariate prediction model of geothermal parameters based on machine learning

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  • Zheng, Shuang-Fei
  • Li, Xu
  • Wang, Meng

Abstract

The geothermal parameters (GTPs, C and λ) are fundamental for characterizing heat storage and conduction in the soil. The measurement for these parameters requires complex mathematical models and stringent conditions. This leads to complicated measuring equipment and high time costs.

Suggested Citation

  • Zheng, Shuang-Fei & Li, Xu & Wang, Meng, 2025. "Multivariate prediction model of geothermal parameters based on machine learning," Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:energy:v:316:y:2025:i:c:s0360544225001392
    DOI: 10.1016/j.energy.2025.134497
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    References listed on IDEAS

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    1. Palacios, Anabel & Cong, Lin & Navarro, M.E. & Ding, Yulong & Barreneche, Camila, 2019. "Thermal conductivity measurement techniques for characterizing thermal energy storage materials – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 32-52.
    2. Dhiman, Harsh S. & Deb, Dipankar & Guerrero, Josep M., 2019. "Hybrid machine intelligent SVR variants for wind forecasting and ramp events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 369-379.
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