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Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization

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  • Baruque, Bruno
  • Porras, Santiago
  • Jove, Esteban
  • Calvo-Rolle, José Luis

Abstract

In recent years, the use of renewable energies has been promoted in most of developed countries due to the climate change threat. In this scenario, the importance of geothermal installations has increased. This paper focuses on a heat exchanger present on a geothermal installation. The main aim is to achieve an accurate prediction system using the previous readings of some of the sensors located along the heat exchanger. Different time series modeling techniques were applied obtaining satisfactory results in the prediction of the heat exchanger state during one year. This prediction is made 1 h, 3 h and 6 h in advance. Also, a strong correlation between the sensor readings is concluded, offering the possibility to dispense some of them.

Suggested Citation

  • Baruque, Bruno & Porras, Santiago & Jove, Esteban & Calvo-Rolle, José Luis, 2019. "Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization," Energy, Elsevier, vol. 171(C), pages 49-60.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:49-60
    DOI: 10.1016/j.energy.2018.12.207
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    References listed on IDEAS

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    Cited by:

    1. Khaled Salhein & Javed Ashraf & Mohamed Zohdy, 2021. "Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model," Energies, MDPI, vol. 14(16), pages 1-12, August.
    2. Zhengchao Xie & Xiao Wang & Lijun Zheng & Hao Chang & Fei Wang, 2022. "A Period-Based Neural Network Algorithm for Predicting Building Energy Consumption of District Heating," Energies, MDPI, vol. 15(17), pages 1-22, August.
    3. Yu, Ruyang & Zhang, Kai & Ramasubramanian, Brindha & Jiang, Shu & Ramakrishna, Seeram & Tang, Yuhang, 2024. "Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China," Energy, Elsevier, vol. 296(C).

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