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Prediction of soil temperatures for shallow geothermal applications in Turkey

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  • Yener, Deniz
  • Ozgener, Onder
  • Ozgener, Leyla

Abstract

In this study, soil temperatures at different depths in Turkey's different regions were investigated theoretically. Soil temperature data are critical for different research interests such as ecology, biology, technique processes, forestry, agriculture, energy, food sector, ground heat exchanger applications, thermal energy storage applications, and so forth. This investigation gives information related to the prediction of soil temperature's dependence with depth and time especially for shallow geothermal applications. Soil temperature values depend on a great deal of varied parameters such as thermal conductivity, short term climatic conditions and moisture content. The main issue is that despite these temperatures are extremely important values, they can not be obtained in a short time. Due to this reason, we study a mathematical model related to the prediction of soil temperature. Within this context, 81 cities and their approximately 300.000 data, both, monthly air and soil temperatures between 1960 and 2015 were studied and finally seven regions in Turkey were investigated and final average soil temperature values were achieved. Measured data taken from the Izmir State Meteorological Station, and predicted soil temperatures at depths of 5cm, 10cm, 20cm, 50cm, and 100cm were analyzed for each region in Turkey according to data obtained fifty years ago. Finally, at depths of 5cm, 10cm, 20cm, 50cm and 100cm, the maximum average percentage errors in Turkey were 16%, 14.8%, 13.5%, 14.4%, 13.9% respectively. In conclusion, we evaluate the relationship between ambient air temperatures and soil temperatures in terms of depths from 5 to 3000cm.

Suggested Citation

  • Yener, Deniz & Ozgener, Onder & Ozgener, Leyla, 2017. "Prediction of soil temperatures for shallow geothermal applications in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 71-77.
    • Handle: RePEc:eee:rensus:v:70:y:2017:i:c:p:71-77
    DOI: 10.1016/j.rser.2016.11.065
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    References listed on IDEAS

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    Cited by:

    1. Sung-Woo Cho & Pyeongchan Ihm, 2018. "Development of a Simplified Regression Equation for Predicting Underground Temperature Distributions in Korea," Energies, MDPI, vol. 11(11), pages 1-18, October.
    2. Mohammad Taghi Sattari & Anca Avram & Halit Apaydin & Oliviu Matei, 2020. "Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models," Mathematics, MDPI, vol. 8(9), pages 1-21, August.
    3. Xing, Lu & Li, Liheng & Gong, Jiakang & Ren, Chen & Liu, Jiangyan & Chen, Huanxin, 2018. "Daily soil temperatures predictions for various climates in United States using data-driven model," Energy, Elsevier, vol. 160(C), pages 430-440.
    4. Zhang, Guanshan & Ding, Xiaoming & Li, Tianhua & Pu, Wenyang & Lou, Wei & Hou, Jialin, 2020. "Dynamic energy balance model of a glass greenhouse: An experimental validation and solar energy analysis," Energy, Elsevier, vol. 198(C).
    5. Cai, Wanlong & Wang, Fenghao & Chen, Chaofan & Chen, Shuang & Liu, Jun & Ren, Zhanli & Shao, Haibing, 2022. "Long-term performance evaluation for deep borehole heat exchanger array under different soil thermal properties and system layouts," Energy, Elsevier, vol. 241(C).

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