Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction model (IGM (1,1)) was applied to perform the annual geothermal energy installation capacity prediction for the top 10 countries based on installed power generation capacity evaluated at the end of 2021, namely the United States, Indonesia, Philippines, Turkey, New Zealand, Mexico, Italy, Kenya, Iceland, and Japan, for the next nine years for the period from 2022 through 2030. These data can be used by future researchers in the field. Separately, datasets from 2000 to 2021 were collected for each country’s geothermal energy installation capacity to build a model which can accurately predict the annually geothermal energy installation capacity by 2030. The IGM (1,1) model used a small dataset of 22 data points, with one point denoting one year (i.e., 22 years), to predict the capacity of geothermal energy installations for the next nine years. Following that, the model was implemented for each dataset in MATLAB, where appropriate, and the model accuracy was evaluated. Ten separate geothermal energy installation capacity datasets were used to validate the improved model, and these datasets further demonstrated the overall improved model’s accuracy. The results prove that the prediction accuracy of the IGM (1,1) model outperforms the benchmark conventional GM (1,1) model, thereby enhancing the overall accuracy of the GM (1,1) model. The IGM (1,1) model ensures error reduction, suggesting that it is an effective and promising tool for accurate short-term prediction. The results reveal the 2030 geothermal energy installation capacity rankings.
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.
This paper presents a mathematical model of heat transfer behavior between the liquid inside vertical underground geothermal pipes and the surrounding ground for heating (in the winter) and cooling (in the summer) modes in a ground heat exchanger (GHE) that can optimize its output temperature. The GHE’s output temperature reaches the appropriate value when the water velocity is lowered enough. Subsequently, the proposed model was applied to a case study of a 400-ton geothermal heat pump system (GHPS) at Oakland University, in both the heating and cooling modes, to assess its validity and improve the GHE’s performance. The model was implemented in MATLAB using an ordinary differential equation (ODE) solver. Four different water velocities were used to demonstrate the significant effect of velocity on the loop exit temperature. Model predictive control (MPC) was designed to optimize the GHE’s output temperature by controlling the water velocity, which could reduce the energy consumption used for heat and water circulating pumps. The results reveal that the acceptable range of the water velocity for Oakland University’s GHE was between 0.35 and 0.45 m/s, which ensured that the heat pump system delivered the proper temperature to provide the Human Health Building (HHB) with a comfortable temperature regardless of the season. The suggested water velocity ranges in vertical single U-tube pipes with diameters of De 25 mm, De 32 mm, and De 40 mm are between 0.33 and 0.43 m/s, 0.35 to 0.45 m/s, and 0.38 to 0.48 m/s, respectively.
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