The performance prediction of ground source heat pump system based on monitoring data and data mining technology

This paper studies the performance prediction of ground source heat pump (GSHP) systems by real-time monitoring data and data-driven models. A GSHP system, which is installed in an office building of Shaoxing (29.42°N, 120.16°E), China, is real-time monitored from Nov. 2012 to Mar. 2015. Data mining (DM) technologies were simultaneously applied to process the monitoring data and find the required inputs for data-driven models. Back-propagation Neural Network (BPNN) algorithm was selected from six classical sorting algorithms to establish the data-driven models. The performance of the GSHP system from Nov. 2012 to Mar. 2015 was evaluated by the monitoring data. And the long-term performance was predicted by the data-driven models. The monitoring results show that the application effectiveness of the GSHP system is unsatisfied because of the high pumping power. Moreover, the relationship between the short-term and long-term performance of GSHP system is investigated for the purpose of predicting the long-term performance of GSHP system by a short-term monitoring data. The monitoring data of different days in several modes are needed to predict the long-term performance of GSHP system under a certain deviation.