Machine-learning-based performance prediction of the energy pile heat pump system

Abstract

The coefficient of performance (COP) is one of the important indicators for evaluating ground source heat pump systems, and a certain amount of machine learning algorithms have been applied for COP prediction. However, most of the current models use a large number of input variables, and it may be difficult to obtain all the required parameters for diagnosis of the sensors. Most of the influencing factors contain fluid temperature, which is not easy to measure, and there is a risk of leakage when sensors are deployed in the system. In this work, two-year field tests were conducted based on an energy pile heat pump system, where in situ results were used as sample points, and the more easily measured ambient temperature and humidity, room temperature and humidity, and hourly power consumption were used as input parameters. Spearman correlation analysis is used to rank the influencing factors, and the prediction of COP was developed by a hybrid artificial neural network (ANN), optimized via the particle swarm optimization algorithm (PSO), and finally compared with empirical models and their updates. Results showed that among the input variables, the most and least important factors affecting the COP are the ambient temperature and ambient humidity, respectively. The prediction results of PSO-ANN were in the best agreement with the measured values, and the accuracy was higher than that of the empirical regression models. The proposed method noted in this study can be used as a supplement to the current COP prediction.