A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features
Author
Abstract
Suggested Citation
DOI: 10.1016/j.energy.2022.123738
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- 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).
- Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
- Yan, Qing-dong & Chen, Xiu-qi & Jian, Hong-chao & Wei, Wei & Wang, Wei-da & Wang, Heng, 2022. "Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck," Energy, Elsevier, vol. 238(PC).
- Hao, Ying & Dong, Lei & Liang, Jun & Liao, Xiaozhong & Wang, Lijie & Shi, Lefeng, 2020. "Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid," Renewable Energy, Elsevier, vol. 155(C), pages 1191-1210.
- Robinson, P. M., 1977. "The estimation of a nonlinear moving average model," Stochastic Processes and their Applications, Elsevier, vol. 5(1), pages 81-90, February.
- Lin, Xinyou & Xia, Yutian & Huang, Wei & Li, Hailin, 2021. "Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle," Energy, Elsevier, vol. 224(C).
- Felipe Gonzalez & Marc Petit & Yannick Perez, 2021. "Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies," Post-Print hal-03363782, HAL.
- Chong, Terence Tai-Leung, 2000.
"Estimating the differencing parameter via the partial autocorrelation function,"
Journal of Econometrics, Elsevier, vol. 97(2), pages 365-381, August.
- Terence Tai-Leung, Chong, 1998. "Estimating the Differencing Parameter Via the Partial Autocorrelation Function," Departmental Working Papers _088, Chinese University of Hong Kong, Department of Economics.
- Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
- Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
- Tarroja, Brian & Hittinger, Eric, 2021. "The value of consumer acceptance of controlled electric vehicle charging in a decarbonizing grid: The case of California," Energy, Elsevier, vol. 229(C).
- Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2021. "Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data," Energy, Elsevier, vol. 225(C).
- Han, Xiaojuan & Wei, Zixuan & Hong, Zhenpeng & Zhao, Song, 2020. "Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain," Renewable Energy, Elsevier, vol. 161(C), pages 419-434.
- Barman, Mayur & Dev Choudhury, N.B. & Sutradhar, Suman, 2018. "A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India," Energy, Elsevier, vol. 145(C), pages 710-720.
- Álvarez, Roberto & Zubelzu, Sergio & Díaz, Guzmán & López, Alberto, 2015. "Analysis of low carbon super credit policy efficiency in European Union greenhouse gas emissions," Energy, Elsevier, vol. 82(C), pages 996-1010.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yin, Linfei & Zhang, Yifan, 2024. "Particle swarm optimization based on data driven for EV charging station siting," Energy, Elsevier, vol. 310(C).
- Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
- Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
- Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
- 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).
- Wang, Chao & Zhang, Xin & Yun, Xiang & Meng, Xiangfei & Fan, Xingming, 2023. "Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm," Energy, Elsevier, vol. 285(C).
- 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).
- 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).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
- 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).
- 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).
- 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).
- Zhang, Chengquan & Kitamura, Hiroshi & Goto, Mika, 2024. "Feasibility of vehicle-to-grid (V2G) implementation in Japan: A regional analysis of the electricity supply and demand adjustment market," Energy, Elsevier, vol. 311(C).
- Wang, Ying & Li, Hongmin & Jahanger, Atif & Li, Qiwei & Wang, Biao & Balsalobre-Lorente, Daniel, 2024. "A novel ensemble electricity load forecasting system based on a decomposition-selection-optimization strategy," Energy, Elsevier, vol. 312(C).
- Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
- Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
- Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
- Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
- Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).
- Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
- Deyun Wang & Yanling Liu & Zeng Wu & Hongxue Fu & Yong Shi & Haixiang Guo, 2018. "Scenario Analysis of Natural Gas Consumption in China Based on Wavelet Neural Network Optimized by Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
- Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
- S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
- Qi, Kaijian & Zhang, Weigang & Zhou, Wei & Cheng, Jifu, 2022. "Integrated battery power capability prediction and driving torque regulation for electric vehicles: A reduced order MPC approach," Applied Energy, Elsevier, vol. 317(C).
- Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
- Jan Figgener & Jonas van Ouwerkerk & David Haberschusz & Jakob Bors & Philipp Woerner & Marc Mennekes & Felix Hildenbrand & Christopher Hecht & Kai-Philipp Kairies & Oliver Wessels & Dirk Uwe Sauer, 2024. "Multi-year field measurements of home storage systems and their use in capacity estimation," Nature Energy, Nature, vol. 9(11), pages 1438-1447, November.
- Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
- Park, Sung-Won & Son, Sung-Yong, 2023. "Techno-economic analysis for the electric vehicle battery aging management of charge point operator," Energy, Elsevier, vol. 280(C).
More about this item
Keywords
Electric vehicle; Power demand prediction; Periodic feature; Deep learning; SARIMA;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006417. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.