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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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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    3. Ozgener, Onder & Ozgener, Leyla, 2015. "Modeling of driveway as a solar collector for improving efficiency of solar assisted geothermal heat pump system: a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 210-217.
    4. Jacobsson, Staffan & Lauber, Volkmar, 2006. "The politics and policy of energy system transformation--explaining the German diffusion of renewable energy technology," Energy Policy, Elsevier, vol. 34(3), pages 256-276, February.
    5. Ozgener, Leyla, 2011. "A review on the experimental and analytical analysis of earth to air heat exchanger (EAHE) systems in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4483-4490.
    6. Florides, Georgios & Kalogirou, Soteris, 2007. "Ground heat exchangers—A review of systems, models and applications," Renewable Energy, Elsevier, vol. 32(15), pages 2461-2478.
    7. Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, vol. 9(9), pages 1-21, August.
    8. Ozgener, Leyla & Ozgener, Onder, 2009. "Monitoring of energy exergy efficiencies and exergoeconomic parameters of geothermal district heating systems (GDHSs)," Applied Energy, Elsevier, vol. 86(9), pages 1704-1711, September.
    9. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
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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.

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