Research & Publications

PhD Students

  • Eloi Campagne – Graph Neural Networks: applications to net demand
  • Guillaume Principato – Hierarchical Conformal Predictions: applications to electric vehicle charging demand

Papers

  • Amara-Ouali, Y., Goude, Y., Doumèche, N., Veyret, P., Thomas, A., Hebenstreit, D., … & Phe-Neau, T. (2023). Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge. arXiv preprint arXiv:2306.06142. <https://arxiv.org/pdf/2306.06142.pdf>
  • Amara-Ouali, Y., Goude, Y., Massart, P., Poggi, J. M., & Yan, H. (2021). A review of electric vehicle load open data and models. Energies, 14(8), 2233. <https://doi.org/10.3390/en14082233> – pdf
  • Amara-Ouali, Y., Fasiolo, M., Goude, Y., & Yan, H. (2022). Daily peak electrical load forecasting with a multi-resolution approach. International Journal of Forecasting. <https://doi.org/10.1016/j.ijforecast.2022.06.001> – pdf
  • Amara-Ouali, Y., Goude, Y., Hamrouche, B., & Bishara, M. (2022, June). A benchmark of electric vehicle load and occupancy models for day-ahead forecasting on open charging session data. In Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (pp. 193-207). <https://doi.org/10.1145/3538637.3538850>
  • Amara-Ouali Y, Hamrouche B, Principato G, Goude Y. Quantifying the Uncertainty of Electric Vehicle Charging with Probabilistic Load Forecasting. World Electric Vehicle Journal. 2025; 16(2):88. <https://doi.org/10.3390/wevj16020088>
  • Principato, G., Amara-Ouali, Y., Goude, Y., Hamrouche, B., Poggi, J. M., & Stoltz, G. (2024). Conformal Prediction for Hierarchical Data. arXiv preprint <arXiv:2411.13479>.
  • Campagne, E., Amara-Ouali, Y., Goude, Y., & Kalogeratos, A. (2024). Leveraging Graph Neural Networks to Forecast Electricity Consumption. arXiv preprint <arXiv:2408.17366>.

Posters

  • Amara-Ouali, Y., Massart, P., Poggi, J. M., Goude, Y., & Yan, H. (2021, June). A review of electric vehicle charging session open data: Poster. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems (pp. 278-279). <https://doi.org/10.1145/3447555.3466568> – pdf

Presentations

  • JDS 2025 – Deep Learning applied to hierarchical forecasting – materials.pdf

PhD Manuscript

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