In this study, the rotor torque of wind turbines has been predicted using machine learning approach based on real time data which have been collected for the designed small scale Savonius and four leaves rotors. The tip speed ratio (TSR) has been selected as the main input parameter in machine learning modelling technique which are linear regression (LR), support vector machine (SVM) regression and Gaussian process (GP) regression. The hyperparameter of these models have been defined by grid search method. RMSE, determination coefficient, MSE and MAE have been used to evaluate the predictive performance of the models to experimental data. The rotor torque modelling results show the efficiency of wind turbines can be maximized with high estimation accuracy of models. On the other hand, it has been also observed that torque of the Savonius type wind turbine is higher than the four leaves turbine

In this study, an innovative system was designed to cool exhaust gases to be used in exhaust gas recirculation (EGR) systems of diesel engines. The designed system uses a 12 V electric pump and fan. PID (Proportional Integral Derivative) controllers are designed for these elements and optimization method is used for the controller parameters. For the amount and temperature of the gases entering the intake manifold, their effects on engine NOx and brake specific fuel consumption (BSFC) under different acclimation conditions were analyzed. A mathematical model was developed using databased methods. The reference EGR flow rate and temperature value for different engine operating conditions have been defined for the control system designed thanks to the obtained mathematical model. The compatibility of models defined with Adaptive Neural Fuzzy Inference System (ANFIS) with real data was statistically analyzed.