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An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations

Author

Listed:
  • Buzna, Luboš
  • De Falco, Pasquale
  • Ferruzzi, Gabriella
  • Khormali, Shahab
  • Proto, Daniela
  • Refa, Nazir
  • Straka, Milan
  • van der Poel, Gijs

Abstract

Transportation electrification is a valid option for supporting decarbonization efforts but, at the same time, the growing number of electric vehicles will produce new and unpredictable load conditions for the electrical networks. Accurate electric vehicle load forecasting becomes essential to reduce adverse effects of electric vehicle integration into the grid. In this paper, a methodology dedicated to probabilistic electric vehicle load forecasting for different geographic regions is presented. The hierarchical approach is applied to decompose the problem into sub-problems at low-level regions, which are resolved through standard probabilistic models such as gradient boosted regression trees, quantile regression forests and quantile regression neural networks, coupled with principal component analysis to reduce the dimensionality of the sub-problems. The hierarchical perspective is then finalized to forecast the aggregate load at a high-level geographic region through an ensemble methodology based on a penalized linear quantile regression model. This paper brings, as relevant contributions, the development of hierarchical probabilistic forecasting framework, its comparison with non-hierarchical frameworks, and the assessment of the role of data dimensionality refduction. Extensive experimental results based on actual electric vehicle load data are presented which confirm that the hierarchical approaches increase the skill of probabilistic forecasts up to 9.5% compared with non-hierarchical approaches.

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  • Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317207
    DOI: 10.1016/j.apenergy.2020.116337
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    5. Jamali Jahromi, Ali & Mohammadi, Mohammad & Afrasiabi, Shahabodin & Afrasiabi, Mousa & Aghaei, Jamshid, 2022. "Probability density function forecasting of residential electric vehicles charging profile," Applied Energy, Elsevier, vol. 323(C).
    6. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    7. Kreft, Markus & Brudermueller, Tobias & Fleisch, Elgar & Staake, Thorsten, 2024. "Predictability of electric vehicle charging: Explaining extensive user behavior-specific heterogeneity," Applied Energy, Elsevier, vol. 370(C).
    8. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    9. Zhou, Kaile & Hu, Dingding & Li, Fangyi, 2022. "Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data," Transport Policy, Elsevier, vol. 125(C), pages 164-178.
    10. Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
    11. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    12. Liansong Yu & Xiaohu Ge, 2024. "Time-Series Prediction of Electricity Load for Charging Piles in a Region of China Based on Broad Learning System," Mathematics, MDPI, vol. 12(13), pages 1-12, July.
    13. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    14. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    15. Qian Wang & Xiaolong Yang & Xiaoyu Yu & Jingwen Yun & Jinbo Zhang, 2023. "Electric Vehicle Participation in Regional Grid Demand Response: Potential Analysis Model and Architecture Planning," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
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