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Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

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  1. 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).
  2. Sanchari Deb & Xiao-Zhi Gao, 2022. "Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest," Energies, MDPI, vol. 15(10), pages 1-18, May.
  3. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
  4. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
  5. Roy, Avipsa & Law, Mankin, 2022. "Examining spatial disparities in electric vehicle charging station placements using machine learning," SocArXiv hvw2t_v1, Center for Open Science.
  6. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
  7. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
  8. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J., 2024. "From user to operator: Rationalizing the charging infrastructure deployment. A case study of Berlin," Applied Energy, Elsevier, vol. 376(PB).
  9. Francesco Lo Franco & Mattia Ricco & Vincenzo Cirimele & Valerio Apicella & Benedetto Carambia & Gabriele Grandi, 2023. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach," Energies, MDPI, vol. 16(4), pages 1-27, February.
  10. Athanasios Paraskevas & Dimitrios Aletras & Antonios Chrysopoulos & Antonios Marinopoulos & Dimitrios I. Doukas, 2022. "Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning," Energies, MDPI, vol. 15(7), pages 1-24, March.
  11. Roy, Avipsa & Law, Mankin, 2022. "Examining spatial disparities in electric vehicle charging station placements using machine learning," SocArXiv hvw2t, Center for Open Science.
  12. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
  13. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.
  14. Rehman, Waqas ur & Bo, Rui & Mehdipourpicha, Hossein & Kimball, Jonathan W., 2022. "Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation," Applied Energy, Elsevier, vol. 313(C).
  15. Dietmar Göhlich & Kai Nagel & Anne Magdalene Syré & Alexander Grahle & Kai Martins-Turner & Ricardo Ewert & Ricardo Miranda Jahn & Dominic Jefferies, 2021. "Integrated Approach for the Assessment of Strategies for the Decarbonization of Urban Traffic," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
  16. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
  17. Adrian Ostermann & Yann Fabel & Kim Ouan & Hyein Koo, 2022. "Forecasting Charging Point Occupancy Using Supervised Learning Algorithms," Energies, MDPI, vol. 15(9), pages 1-23, May.
  18. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  19. Philipp A. Friese & Wibke Michalk & Markus Fischer & Cornelius Hardt & Klaus Bogenberger, 2021. "Charging Point Usage in Germany—Automated Retrieval, Analysis, and Usage Types Explained," Sustainability, MDPI, vol. 13(23), pages 1-26, November.
  20. Subhaditya Shom & Kevin James & Mahmoud Alahmad, 2022. "Understanding the Correlation of Demographic Features with BEV Uptake at the Local Level in the United States," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
  21. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
  22. Winschermann, Leoni & Bañol Arias, Nataly & Hoogsteen, Gerwin & Hurink, Johann, 2023. "Assessing the value of information for electric vehicle charging strategies at office buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  23. 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).
  24. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhao, Yiwen & Zhan, Weipeng, 2023. "Stacking regression technology with event profile for electric vehicle fast charging behavior prediction," Applied Energy, Elsevier, vol. 336(C).
  25. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
  26. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.
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