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A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features

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  • Ren, Fei
  • Tian, Chenlu
  • Zhang, Guiqing
  • Li, Chengdong
  • Zhai, Yuan

Abstract

Accurate power demand prediction of electrical vehicles (EVs) is crucial to power grid operation. To fully utilize the existing knowledge of EVs’ power demand and further improve the prediction accuracy, this paper proposes a hybrid method for power demand prediction of EVs based on Auto-Regressive Integrated Moving Average (SARIMA) and deep learning with the integration of periodic features. First, the general linear trend of power demand is extracted by SARIMA; then, the residual non-linear components are obtained by eliminating the linear trend from the original power demand. Meanwhile, the periodic features of the non-linear component are determined according to the periodic parameters of the SARIMA. Afterward, the non-linear components are approximated by Long-Short Term Memory (LSTM), and the periodic features of the non-linear components are taken as a part of the inputs of the LSTM. Finally, the extracted linear trend and the predicted non-linear components are combined to generate the final prediction results. To verify the effectiveness of the proposed method, three experiments are conducted on a real EV charging station. The experimental results indicate that the proposed method significantly improves the prediction accuracy compared with other popular data-driven models.

Suggested Citation

  • Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222006417
    DOI: 10.1016/j.energy.2022.123738
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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. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. 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).
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    Full references (including those not matched with items on IDEAS)

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    5. 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).

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