Intelligence-based prediction of coefficient of performance for a novel high-temperature industrial heat pump: Comparative performance of ANN and ANFIS models

Abstract

This study presents a comparative evaluation of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) approaches for predicting the coefficient of performance (COP) of the HoegTemp, a high-temperature heat pump (HTHP) based on a Stirling cycle (SC) with a design heat capacity of 400 kW. Experimental tests were conducted at the IVAR biogas facility in Stavanger, Norway. This study employed a feedforward backpropagation neural network (FFBPNN) model with one and two hidden layers, with various numbers of neurons and three activation functions, as well as the ANFIS approach, to estimate the COP of the SC-HTHP. The FFBPNN model used the Levenberg-Marquardt (LM) and Bayesian regularization (BR) training algorithms, while the ANFIS model utilized a hybrid optimization method and grid partitioning. The ANN and ANFIS models were evaluated using the following input variables: temperature ratio (1.4–1.6 K/K), average source temperature (21–22 °C), average sink temperature (139–199 °C) and hot water inlet temperature (137–197 °C), with COP as the output variable. The results demonstrated that the FFBP-ANN model exhibited superior predictive accuracy compared to the ANFIS model, achieving R² = 0.9999, MSE = 0.00010, MAE = 0.00804, and RMSE = 0.01000, whereas the ANFIS approach resulted in R² = 0.9863, MSE = 0.00019, MAE = 0.01114, and RMSE = 0.01392. The optimal ANN topology was 4–23-16–1 with tansig–logsig–purelin activation functions. In contrast, the best membership functions selected for ANFIS were Gaussian for the input layer and constant for the output layer.