Artificial neural networks for automotive air-conditioning systems performance prediction

Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models

“…Kamar et al [14] built an ANN model for a standard airconditioning system of a passenger car to predict the cooling capacity, compressor power input and the COP of the automotive airconditioning (AAC) system. The input layer parameters of ANN model were the compressor speed, air temperature at evaporator inlet, air temperature at condenser inlet and air velocity at evaporator inlet.…”

Performance evaluation of an R134a automotive heat pump system for various heat sources in comparison with baseline heating system

“…They performed transient and steady-state tests under various operating conditions, finding that the use of their system improved heating capacity compared to the baseline heating system. Hosoz and Ertunc [14] and Kamar et al [15] modelled AAC systems using artificial neural networks (ANN) for various compressor speeds, air temperatures and velocities at evaporator inlet and air temperatures at condenser inlet. They determined that the developed ANN models predicted the performance of AAC systems with a high accuracy.…”

Optimisation of Refrigeration System with Two-Stage and Intercooler Using Fuzzy Logic and Genetic Algorithm

“…And they have changed the compressor speed, air temperature at evaporator inlet, air temperature at condenser inlet and air velocity at evaporator inlet. Root mean square error (RMSE) was obtained in the range of %0.33-0.95 and mean square error (MSE) were obtained between 1.09x105 and 9.05x105 in their study [4]. Esen and Inalli have been examined to estimation of performance of a vertical ground source heat pump system with artificial neural network and an adaptive neuro-fuzzy inference system.…”