A Hybrid LSTM-DNN model, predicting fuel consumption of dump trucks in open-pit mining
Abstract
A Hybrid LSTM-DNN model, predicting fuel consumption of dump trucks in open-pit mining
Incoming article date: 03.12.2023Fuel efficiency of dump trucks is affected by real world variables such as vehicle parameters, road conditions, weather parameters, and driver behavior. Predicting fuel consumption per trip using dynamic road condition data can effectively reduce the cost and time associated with on-road testing. This paper proposes new models for predicting fuel consumption of dump trucks in surface mining operations. The models combine locally collected data from dump truck sensors and analyze it to enhance their capabilities. The architectural design consists of two distinct parts, initially based on dual Long-term Short-Term Memories (LSTMs) and dual dense layers of Deep Neural Networks (DNNs). The new hybrid architecture improves the performance of the proposed model compared to other models, especially in terms of accuracy measurement. The MAE, RMSE, MSE and R2 scores indicate high prediction accuracy.
Keywords: LSTM algorithm, DNN, density, prediction, fuel consumption, quarries