Journal Article

TSF-transformer: a time series forecasting model for exhaust gas emission using transformer

2023

Overview

Monitoring and prediction of exhaust gas emissions for heavy trucks is a promising way to solve environmental problems. However, the emission data acquisition is time delayed and the pattern of emission is usually irregular, which makes it very difficult to accurately predict the emission state. To deal with these problems, in this paper, we interpret emission prediction as a time series prediction problem and explore a deep learning model, a time-series forecasting Transformer (TSF-Transformer) for exhaust gas emission prediction. The exhaust emission of the heavy truck is not directly predicted, but indirectly predicted by predicting the temperature and pressure changes of the exhaust pipe under the working state of the truck. The basis of our research is based on real-time data feeds from temperature and pressure sensors installed on the exhaust pipe of approximately 12,000 heavy trucks. Therefore, the task of time series forecasting consists of two key stages: monitoring and prediction. The former utilizes the server to receive the data sent by the sensors in real-time, and the latter uses these data as samples for network training and testing. The training of the network throughout the prediction process is done in an unsupervised manner. Also, to visualize the forecast results, we weight the forecast data with the truck trajectories and present them as heatmaps. To the best of our knowledge, this is the first case of using the Transformer as the core component of the prediction model to complete the task of exhaust emissions prediction from heavy trucks. Experiments show that the prediction model outperforms other state-of-the-art methods in prediction accuracy.

• Publisher: Springer Nature B.V
• Subject: Data acquisition / Exhaust emission / Exhaust gases / Exhaust pipes / Forecasting / Heavy duty trucks / Mathematical models / Monitoring / Prediction models / Predictions / Pressure sensors / Real time / Time series / Training / Transformers / Trucks