Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system

Abstract

A ground source heat pump system (GSHP) with 450 RT capacity composed of ten heat pump units provides the heating and cooling energy to an entire hospital building. The seasonal heating performance of 3.21 and system operation properties of the system were analyzed using in situ monitoring data from Nov. 2016 to Mar. 2017. On this basis, hourly GSHP system performance prediction models applying a multiple linear regression (MLR) and an artificial neural network (ANN) were developed. The quantitative effects of influencing variables on the system performance, including the entering source and load water temperatures (EST, ELT) were analyzed by elaborated MLR model with statistical significance. The prediction accuracy was 3.56% by the MLR, and 1.75% by the ANN, based on the coefficient of variation of root mean squared error (CVRMSE) without overall bias. These prediction models can be used as a baseline for the measurement and verification (M&V) of possible future energy conservation measures and real-time performance monitoring to check malfunction of the system.