Predicting Energy Consumption for Heavy-Duty Vehicles (in collaboration with Volvo)
| Title | Predicting Energy Consumption for Heavy-Duty Vehicles (in collaboration with Volvo) |
|---|---|
| Summary | Develop machine learning methods to forecast energy consumption for heavy-duty vehicles |
| Keywords | |
| TimeFrame | Fall 2023 |
| References | Koprinska, I., Wu, D., & Wang, Z. (2018, July). Convolutional neural networks for energy time series forecasting. In 2018 international joint conference on neural networks (IJCNN) (pp. 1-8). IEEE.
Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., ... & Zhang, Q. (2020). Spectral temporal graph neural network for multivariate time-series forecasting. Advances in neural information processing systems, 33, 17766-17778. Geng, X., He, X., Xu, L., & Yu, J. (2022). Graph correlated attention recurrent neural network for multivariate time series forecasting. Information Sciences, 606, 126-142. Nan, S., Tu, R., Li, T., Sun, J., & Chen, H. (2022). From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus. Energy, 261, 125188. De Cauwer, C., Verbeke, W., Coosemans, T., Faid, S., & Van Mierlo, J. (2017). A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies, 10(5), 608. Arastehfar, S., Matinkia, M., & Jabbarpour, M. R. (2022). Short-term residential load forecasting using graph convolutional recurrent neural networks. Engineering Applications of Artificial Intelligence, 116, 105358. Shchetinin, E. Y. (2018). Cluster-based energy consumption forecasting in smart grids. In Distributed Computer and Communication Networks: 21st International Conference, DCCN 2018, Moscow, Russia, September 17–21, 2018, Proceedings 21 (pp. 445-456). Springer International Publishing. Le, T., Vo, M. T., Kieu, T., Hwang, E., Rho, S., & Baik, S. W. (2020). Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors, 20(9), 2668. |
| Prerequisites | |
| Author | |
| Supervisor | Yuantao Fan, Mahmoud Rahat |
| Level | Master |
| Status | Internal Draft |
This project will explore and develop machine learning methods to predict energy consumption for commercial heavy-duty vehicles, e.g. city buses and electric trucks. Interesting research topics include: i) exploring deep neural networks (e.g. CNNs, RNNs, or GNNs) for time-series forecasting; ii) fleet-based regression and interactive clustering; iii) learning energy consumption using spatial-temporal data in a grid map setting.
