Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network

Gray-box model for energy-efficient selection of set point hysteresis in heating, ventilation, air conditioning, and refrigeration controllers

“…Yao et al [15] used a case study to show that a combined forecasting model based on a combination of neural networks and a few other methods is promising for predicting the hourly loads in buildings. Solmaz et al [16] used the same concept of neural networks to predict the hourly cooling load for vehicle cabins.…”

Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks

“…The activation function j is generally the sigmoid or hyperbolic tangent function in the hidden layers, whereas the linear function is also utilized in the output layer [44]. It is vital to normalize the input and output data for the ANN training so as to prevent saturation of the activation function of neurons [44,45]. A suitable number of representative examples of the relevant phenomenon must be selected in order to train the ANN so that it can learn the system behavior with the adaptive modification of its weights [42,44].…”

Real-time thermal load calculation by automatic estimation of convection coefficients

“…Several studies (Li et al, Jan. 2009;Kashiwagi and Tobi, 1993;BenNakhi and Mahmoud, 2004;Sousa et al, 1997;Yao et al, 2004;Solmaz et al, 2014;Fayazbakhsh et al, 2015;Liang and Du, 2005) show that artificial intelligence algorithms such as neural networks, genetic algorithm, and fuzzy logic can help estimate the thermal loads in HVAC-R systems. Such models focus on relating the thermal load to parameters such as the ambient temperature by learning from real-time measurements rather than explicitly using the heat transfer equations.…”