A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization

Abstract

When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future.

Variable Water Flow Control of Hybrid Geothermal Heat Pump System

“…Existing system COPs can be analyzed using measurement equipment or mathematical models. Various monitoring or measurement values and multiple sensors are required to analyze the COP using mathematical models, which is expensive [55]. The COP needs to be predicted using machine learning, which uses fewer monitoring sensors.…”

“…The COPs of GHPs can be calculated theoretically using mathematical models, simulations, machine learning, or measuring instruments. Theoretical calculations using mathematical models are expensive because of the large number of input values required and the need for various measuring instruments to measure them, such as temperature, heat pump power consumption, pressure, water flow rate, and refrigerant flow rate [55]. Long-term field monitoring is the most accurate and reliable method to check operational status to maintain proper performance.…”