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Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model

Author

Listed:
  • Khaled Salhein

    (Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA)

  • Javed Ashraf

    (Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA)

  • Mohamed Zohdy

    (Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA)

Abstract

This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5075-:d:616664
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    References listed on IDEAS

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    1. Kalyana C. Chejarla & Omkarprasad S. Vaidya, 2024. "A hybrid multi-criteria decision-making approach for longitudinal data," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1013-1060, September.

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