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Daily soil temperatures predictions for various climates in United States using data-driven model

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  • Xing, Lu
  • Li, Liheng
  • Gong, Jiakang
  • Ren, Chen
  • Liu, Jiangyan
  • Chen, Huanxin

Abstract

As an important indicator of soil characteristics, soil temperature has a great impact on agricultural production, building energy savings and for shallow geothermal applications. Data-driven models have been developed and achieved good accuracy in monthly soil temperatures or daily soil temperatures predictions of a single site taking air temperature, solar radiant and time as inputs. Models’ accuracy obviously dropped if they are applied for predicting daily soil temperatures for various climates on a continental scale. We proposed a new data-driven model based on the support vector machine (SVM). The new model considers daily soil temperature variations as superposition of annual average ground temperatures predictions (long-term climates impact) and daily ground temperature amplitude predictions (short-term climates impact). Annual average soil temperature are determined by air temperature, solar radiant, wind speed and relative humidity; daily soil temperature amplitudes by air temperature amplitudes, solar radiant and day of year. For daily soil temperature predictions at 16 sites located in arid or dry summer climates, warm climates and snow climates in United States, the new model’s mean absolute error is 1.26 °C and root mean square error is 1.66 °C. Meanwhile, traditional SVM model’s mean absolute error is 2.20 °C and root mean square error is 2.91 °C.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:430-440
    DOI: 10.1016/j.energy.2018.07.004
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    References listed on IDEAS

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    1. Badache, Messaoud & Eslami-Nejad, Parham & Ouzzane, Mohamed & Aidoun, Zine & Lamarche, Louis, 2016. "A new modeling approach for improved ground temperature profile determination," Renewable Energy, Elsevier, vol. 85(C), pages 436-444.
    2. Tsilingiridis, G. & Papakostas, K., 2014. "Investigating the relationship between air and ground temperature variations in shallow depths in northern Greece," Energy, Elsevier, vol. 73(C), pages 1007-1016.
    3. 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.
    4. Zhao, Deyin & Zhong, Ming & Zhang, Xu & Su, Xing, 2016. "Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining," Energy, Elsevier, vol. 102(C), pages 660-668.
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    Cited by:

    1. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(C).
    2. Mercedeh Taheri & Helene Katherine Schreiner & Abdolmajid Mohammadian & Hamidreza Shirkhani & Pierre Payeur & Hanifeh Imanian & Juan Hiedra Cobo, 2023. "A Review of Machine Learning Approaches to Soil Temperature Estimation," Sustainability, MDPI, vol. 15(9), pages 1-26, May.

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