Title :
Performance prediction of ground-coupled heat pump system using NNCA-RBF neural networks
Author :
Guiyang Wang ; Yating Zhang ; Ruihua Wang ; Guang Han
Author_Institution :
Beijing Univ. of Technol., Beijing, China
Abstract :
This paper describes an application of artificial neural networks (ANNs) based on improved Radial Basis Function (NNCA-RBF) to predict performance of a horizontal ground-coupled heat pump (GCHP) system. Performance forecasting is the precondition for the optimal control and energy saving operation of heat pump systems. ANNs have been used in varied applications and they have been shown to be particularly useful in system modeling and system identification. In this study NNCA-RBFNN predictions usually agree well with the experimental values with correlation coefficients in the range of 0.9967-0.9998, mean relative errors in the range of 1.02-4.83% and root mean square errors in the range of 0.0147-0.058. The NNCA-RBFNN approach shows high accuracy and reliability for predicting the performance of GCHP systems.
Keywords :
building management systems; correlation methods; energy conservation; forecasting theory; ground source heat pumps; optimal control; radial basis function networks; ANN; NNCA-RBF neural networks; artificial neural networks; correlation coefficients; energy saving operation; horizontal GCHP system; horizontal ground-coupled heat pump system; improved radial basis function; mean relative errors; optimal control; performance forecasting; performance prediction; root mean square errors; system identification; system modeling; Artificial neural networks; Heat pumps; Heating; Refrigerants; Temperature measurement; Artificial neural network; Coefficient of performance; Ground-coupled heat pump;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561294
