IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v163y2016icp134-141.html

Forecasting the EV charging load based on customer profile or station measurement?

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Liu, Yuechen Sophia & Tayarani, Mohammad & Gao, H. Oliver, 2022. "An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand," Energy, Elsevier, vol. 258(C).
  2. Byungsung Lee & Haesung Lee & Hyun Ahn, 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation," Energies, MDPI, vol. 13(18), pages 1-15, September.
  3. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
  4. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
  5. Prince Aduama & Zhibo Zhang & Ameena S. Al-Sumaiti, 2023. "Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model," Energies, MDPI, vol. 16(3), pages 1-14, January.
  6. Cao, Tingwei & Xu, Yinliang & Liu, Guowei & Tao, Shengyu & Tang, Wenjun & Sun, Hongbin, 2024. "Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station," Applied Energy, Elsevier, vol. 371(C).
  7. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
  8. Dan Zhou & Zhonghao Guo & Yuzhe Xie & Yuheng Hu & Da Jiang & Yibin Feng & Dong Liu, 2022. "Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting," Energies, MDPI, vol. 15(17), pages 1-15, August.
  9. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
  10. Xiong, Yingqi & Wang, Bin & Chu, Chi-cheng & Gadh, Rajit, 2018. "Vehicle grid integration for demand response with mixture user model and decentralized optimization," Applied Energy, Elsevier, vol. 231(C), pages 481-493.
  11. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
  12. Yu, Huaxi & Wang, Ying & Chen, Yinsheng & Li, Qilin & Xiao, Xianyong & Chen, Yunzhu & Li, Shunyi, 2025. "Probabilistic assessment method for evaluation of adjustable capacity of electric vehicle charging stations for volt–var control in distribution networks," Applied Energy, Elsevier, vol. 398(C).
  13. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
  14. Maher Alaraj & Mohammed Radi & Elaf Alsisi & Munir Majdalawieh & Mohamed Darwish, 2025. "Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review," Energies, MDPI, vol. 18(17), pages 1-92, September.
  15. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
  16. Zhuang, Yingrui & Cheng, Lin & Qi, Ning & Wang, Xinyi & Chen, Yue, 2025. "Real-time hosting capacity assessment for electric vehicles: A sequential forecast-then-optimize method," Applied Energy, Elsevier, vol. 380(C).
  17. 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).
  18. Frechter, Yotam & Kuperman, Alon, 2020. "Analysis and design of inductive wireless power transfer link for feedback-less power delivery to enclosed compartment," Applied Energy, Elsevier, vol. 278(C).
  19. Feng, Jian & Yao, Yifan & Liu, Zhenfeng & Liu, Zhenling, 2024. "Electric vehicle charging stations' installing strategies: Considering government subsidies," Applied Energy, Elsevier, vol. 370(C).
  20. Mu Li & Yingqi Liu & Weizhong Yue, 2022. "Evolutionary Game of Actors in China’s Electric Vehicle Charging Infrastructure Industry," Energies, MDPI, vol. 15(23), pages 1-20, November.
  21. Mostafa Majidpour & Hamidreza Nazaripouya & Peter Chu & Hemanshu R. Pota & Rajit Gadh, 2018. "Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System," Forecasting, MDPI, vol. 1(1), pages 1-14, September.
  22. 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).
  23. Shi, Jiaqi & Liu, Nian & Huang, Yujing & Ma, Liya, 2022. "An Edge Computing-oriented Net Power Forecasting for PV-assisted Charging Station: Model Complexity and Forecasting Accuracy Trade-off," Applied Energy, Elsevier, vol. 310(C).
  24. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli, 2021. "Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions," Energies, MDPI, vol. 14(4), pages 1-15, February.
  25. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.
  26. Siow Jat Shern & Md Tanjil Sarker & Mohammed Hussein Saleh Mohammed Haram & Gobbi Ramasamy & Siva Priya Thiagarajah & Fahmid Al Farid, 2024. "Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems," Energies, MDPI, vol. 17(22), pages 1-25, November.
  27. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
  28. Manbachi, M. & Sadu, A. & Farhangi, H. & Monti, A. & Palizban, A. & Ponci, F. & Arzanpour, S., 2016. "Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform," Applied Energy, Elsevier, vol. 169(C), pages 28-39.
  29. Danny García Sánchez & Alejandra Tabares & Lucas Teles Faria & Juan Carlos Rivera & John Fredy Franco, 2022. "A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows," Energies, MDPI, vol. 15(7), pages 1-19, March.
  30. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
  31. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
  32. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
  33. Zhang, Tianren & Huang, Yuping & Liao, Hui & Liang, Yu, 2023. "A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network," Applied Energy, Elsevier, vol. 351(C).
  34. 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).
  35. 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.
  36. Xie, Tuo & Yun, Xinyao & Zhang, Gang & Li, Hua & Zhang, Kaoshe & Wang, Ruogu, 2024. "Charging station cluster load prediction: Spatiotemporal multi-graph fusion technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
  37. Shang, Yitong & Li, Duo & Li, Yang & Li, Sen, 2025. "Explainable spatiotemporal multi-task learning for electric vehicle charging demand prediction," Applied Energy, Elsevier, vol. 384(C).
  38. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
  39. Zhao, Zhonghao & Lee, Carman K.M. & Yan, Xiaoyuan & Wang, Haonan, 2024. "Reinforcement learning for electric vehicle charging scheduling: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
  40. Sun, Lishan & Huang, Yuchen & Liu, Shuli & Chen, Yanyan & Yao, Liya & Kashyap, Anil, 2017. "A completive survey study on the feasibility and adaptation of EVs in Beijing, China," Applied Energy, Elsevier, vol. 187(C), pages 128-139.
  41. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
  42. Xu, Maosheng & Gao, Shan & Zheng, Junyi & Huang, Xueliang & Wu, Chuanshen, 2024. "Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot," Energy, Elsevier, vol. 309(C).
  43. Nandan Gopinathan & Prabhakar Karthikeyan Shanmugam, 2022. "Energy Anxiety in Decentralized Electricity Markets: A Critical Review on EV Models," Energies, MDPI, vol. 15(14), pages 1-40, July.
  44. Zhu, Lijing & Zhang, Qi & Lu, Huihui & Li, Hailong & Li, Yan & McLellan, Benjamin & Pan, Xunzhang, 2017. "Study on crowdfunding’s promoting effect on the expansion of electric vehicle charging piles based on game theory analysis," Applied Energy, Elsevier, vol. 196(C), pages 238-248.
  45. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
  46. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
  47. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.