Instantaneous Power Consumption Prediction with Modified Temporal Convolutional Network for UAVs - Dudukcu, Hatice Vildan; Taskiran, Murat; Kahraman, Nihan (IEEE, 2022)
The widespread use of the Unmanned Aerial Vehicles (UAVs) in many fields of industry such as defense, entertainment and transportation in recent years has led researchers to carry out many studies on UAV controls. One of the most popular of these study topics is the prediction of the instantaneous power consumption of UAVs during flight. In this study, a modified version of the Temporal Convolutional Network (TCN) architecture, which has recently been widely used for time series prediction, is used for prediction the UAVs' power consumption. As a result of the simulation processes performed using the flight data obtained from the DJI Matrice M100 drone, it was observed that the modified TCN model, which has an RMSE value of 0.0635, achieves better predictions compared to the recurrent neural networks and the classical TCN model.
Keywords: deep neural networks; power consumption; recurrent neural networks; temporal convolutional network; unmanned aerial vehicles; Computer Science; Engineering; Telecommunications