View source for Predicting Energy Consumption for Heavy-Duty Vehicles via Time Series Embeddings (in collaboration with Volvo)

{{StudentProjectTemplate
|Summary=Develop deep learning based methods for time series forecasting; explore self-supervised learning methods for multi-variate time series embeddings
|TimeFrame=2024 Fall
|References=Nalmpantis, C., & Vrakas, D. (2019, May). Signal2vec: Time series embedding representation. In International conference on engineering applications of neural networks (pp. 80-90). Cham: Springer International Publishing.

Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25.

Lee, S., Park, T., & Lee, K. (2023). Learning to embed time series patches independently. arXiv preprint arXiv:2312.16427. https://github.com/seunghan96/pits

Luo, D., & Wang, X. (2024). Moderntcn: A modern pure convolution structure for general time series analysis. In The Twelfth International Conference on Learning Representations. https://github.com/luodhhh/ModernTCN?tab=readme-ov-file

Fraikin, A., Bennetot, A., & Allassonnière, S. (2023). T-Rep: Representation Learning for Time Series using Time-Embeddings. arXiv preprint arXiv:2310.04486.

Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2022). Transformers in time series: A survey. arXiv preprint arXiv:2202.07125.

Ahmed, S., Nielsen, I. E., Tripathi, A., Siddiqui, S., Ramachandran, R. P., & Rasool, G. (2023). Transformers in time-series analysis: A tutorial. Circuits, Systems, and Signal Processing, 42(12), 7433-7466.
|Supervisor=Yuantao Fan & TBD
|Level=Master
|Status=Draft
}}
Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This thesis project will explore, develop, and evaluate machine learning-based methods to forecast energy consumption of commercial heavy-duty vehicles, focusing on transformer-based approaches and self-supervised learning for time series embedding representation generation.