Kasislerin Yakıt Tüketimine Etkisinin RNN, LSTM, GRU Tekrarlayan Derin Öğrenme Algoritmaları ile Tespiti.

This study is aimed to determine the effect of vehicle deceleration and acceleration on fuel consumption in the bumps which are used to regulate traffic. For this, real-time fuel consumption and speed data are acquired with Arduino from the OBD-II port of the vehicle drived on routes with bumps. Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning models are developed to predict fuel consumption. The preprocessed data is used to train the models. Hyperparameter optimization is conducted in the developed models. Thus, the number of layers and the units in the layers, the activation functions and the learning rate is specified. The lowest mean square error is obtained as 63? in the validation set. The effects of different speed scenarios on fuel consumption are predicted by using the models. In conclusion, fuel consumption increased between 16.30% and 31.03% during the impact of the bumps, by using the speed and time calculated for the bumps. [ABSTRACT FROM AUTHOR]