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A deep learning based approach for predicting the demand of electric vehicle charge

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Abstract

Predicting the demand for Electric Vehicle charging energy is essential to increase utilization for the company, reduce costs for both car owners and the company and alleviate the burden on the electric grid stations. However, many factors may affect energy consumption at the station level, such as the growing popularity of EVs, time of day plugin, workday, holidays, random consumption, etc. To overcome the above challenges regarding avoiding overcharge, better managing dispatching stations, reducing energy wastage, we perform a comprehensive data analysis on EV charging stations and propose a novel deep learning based approach. Our research is based on the charging data obtained from a Chinese energy service provider, including the stations’ charging process and geographic information. In the forecasting part, we propose Temporal Encoder-Decoder +LSTM (T-LSTM-Enc) Concatenated with Temporal LSTM (T-LSTM-Ori-TimeFeatures) which aim to address the issue of charging demand prediction. The T-LSTM-Enc pre-trains the data to extract hidden relationships, and the T-LSTM-Ori-TimeFeatures capture the time features impacting the change on the charging data. We build our approach using temporal dependencies to apprehend the short-term, long-term, and trend characteristics for charging demand prediction. To show the efficiency of the proposed method, we evaluate our model using the two datasets for energy consumption of EV charging stations, and the results show that our approach gives promising and good performance.

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References

  1. Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting informa UK limited. Econ Rev 29(5–6):594–621. https://doi.org/10.1080/07474938.2010.481556

    Article  Google Scholar 

  2. Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L, Savarese S (2016) Social lSTM: Human trajectory prediction in crowded spaces. IEEE. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 961–971. 10.1109/cvpr.2016.110

  3. Apt KR (2003) Constraint propagation algorithms. Cambridge University Press. Cambridge, New York. 10.1017/cbo9780511615320.007

  4. Bernstein C (2022) What is electric vehicle charging station? (2018). https://whatis.techtarget.com/definition/electric-vehicle-charging-station. Accessed: 2022-02-21

  5. Chen J, Xing H, Yang H, Xu L (2019) Network Traffic Prediction Based on LSTM Networks with Genetic Algorithm. In: International Conference on Signal and Information Processing, Networking and Computers, pp 411–419

  6. Cooley JW, Tukey JW (1965) An algorithm for the machine calculation of complex fourier series. Math Comput 19(90):297–301

    Article  MathSciNet  Google Scholar 

  7. De Gooijer JG, Hyndman RJ (2005) 25 Years of IIF time series forecasting: a selective review. SSRN Electr J. https://doi.org/10.2139/ssrn.748904

    Article  Google Scholar 

  8. Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. JSTOR Econometrica 50(4):987–1007. https://doi.org/10.2307/1912773

    Article  MathSciNet  MATH  Google Scholar 

  9. Gabash A, Alkal M, Li P (2013) Impact of allowed reverse active power flow on planning PVs and BSSs in distribution networks considering demand and EVs growth. Power & Energy Student Summit (PESS) 2013, IEEE Student Branch Bielefeld, pp 11–16

  10. Gerritsma MK, AlSkaif TA, Fidder HA, van Sark WG (2019) Flexibility of electric vehicle demand: analysis of measured charging data and simulation for the future. World Electr Veh J 10(1):14. https://doi.org/10.3390/wevj10010014

    Article  Google Scholar 

  11. Goller C, Küchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. In: IEEE Proceedings of International Conference on Neural Networks (ICNN’96) 1, 347–352 (1996). https://doi.org/10.1109/icnn.1996.548916

  12. Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) LSTM: A search space odyssey institute of electrical and electronics engineers (IEEE). IEEE Trans Neural Netw Learn Syst 28(10):2222–2232. https://doi.org/10.1109/tnnls.2016.2582924

    Article  MathSciNet  Google Scholar 

  13. Heckbert P (1995) Fourier transforms and the fast Fourier transform (FFT) Algorithm. Comput Graph 2:15–463

    Google Scholar 

  14. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  MathSciNet  MATH  Google Scholar 

  15. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  16. Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H (2019) Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 57(6):114–119. https://doi.org/10.1109/mcom.2019.1800155

    Article  Google Scholar 

  17. International Energy Agency (2019) Global EV Outlook 2019: Scaling-up the transition to electric mobility. OECD. https://doi.org/10.1787/35fb60bd-en

    Article  Google Scholar 

  18. Jiming H, Lingyu K, Yaqi S, Ying L, Wenting X, Hao W (2017) A review of demand forecast for charging facilities of electric vehicles. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899x/199/1/012040

    Article  Google Scholar 

  19. Kalimoldayev M, Drozdenko A, Koplyk I, Marinich T, Abdildayeva A, Zhukabayeva T (2020) Analysis of modern approaches for the prediction of electric energy consumption. Open Eng 10(1):350–361. https://doi.org/10.1515/eng-2020-0028

    Article  Google Scholar 

  20. Laib O, Khadir MT, Mihaylova L (2019) Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks. Energy 177:530–542. https://doi.org/10.1016/j.energy.2019.04.075

    Article  Google Scholar 

  21. Lapedes A, Farber R (1987) Nonlinear signal processing using neural networks: Prediction and system modelling

  22. Li Q, Wang X, Wang X, Ma B, Wang C, Shi Y (2021) An encrypted coverless information hiding method based on generative models. Inf Sci 553:19–30. https://doi.org/10.1016/j.ins.2020.12.002

    Article  MathSciNet  MATH  Google Scholar 

  23. Luo Z, Song Y, Hu Z, Xu Z, Yang X, Zhan K (2011) Forecasting charging load of plug-in electric vehicles in China. IEEE. In: 2011 IEEE Power and Energy Society General Meeting, pp 1–8. https://doi.org/10.1109/pes.2011.6039317

  24. Makridakis S, Hibon M (1997) ARMA models and the Box–Jenkins methodology. J Forecast 16(3):147–163. https://doi.org/10.1002/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X

  25. Petrou ZI, Tian Y (2019) Prediction of sea ice motion with convolutional long short-term memory networks. IEEE Trans Geosci Remote Sens 57(9):6865–6876. https://doi.org/10.1109/tgrs.2019.2909057

    Article  Google Scholar 

  26. PJM Interconnection. https://www.pjm.com/. Accessed: 2022-02-21

  27. Poskitt DS, Tremayne AR (1986) The selection and use of linear and bilinear time series models. Int J Forecast 2(1):101–114. https://doi.org/10.1016/0169-2070(86)90033-6

    Article  Google Scholar 

  28. Rutherford MJ, Yousefzadeh V (2011) The impact of Electric Vehicle battery charging on distribution transformers. IEEE. In: 2011 Twenty-Sixth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp 396–400. https://doi.org/10.1109/apec.2011.5744627

  29. Sudhakar C, Kumar AR, Siddartha N, Reddy SV (2018) Workload Prediction using ARIMA Statistical Model and Long Short-Term Memory Recurrent Neural Networks. IEEE. In: 2018 International Conference on Computing, Power and Communication Technologies (GUCON), pp 600–604. https://doi.org/10.1109/gucon.2018.8675117

  30. Temiz A, Guven AN (2016) Assessment of impacts of Electric Vehicles on LV distribution networks in Turkey. IEEE. In: 2016 IEEE International Energy Conference (ENERGYCON), pp 1–6. https://doi.org/10.1109/energycon.2016.7514020

  31. Tong H, Lim KS (1980) Threshold autoregression, limit cycles and cyclical data- with discussion. J R Stat Soc Ser B Stat Methodol 42(3):245–268. https://doi.org/10.1111/j.2517-6161.1980.tb01126.x

    Article  MATH  Google Scholar 

  32. Valsera-Naranjo E, Sumper A, Villafafila-Robles R, Martínez-Vicente D (2012) Probabilistic method to assess the impact of charging of electric vehicles on distribution grids. Energies 5(5):1503–1531. https://doi.org/10.3390/en5051503

    Article  Google Scholar 

  33. Wang X, Wang X, Ma B, Li Q, Shi YQ (2021) High precision error prediction algorithm based on ridge regression predictor for reversible data hiding. IEEE Signal Process Lett 28:1125–1129. https://doi.org/10.1109/LSP.2021.3080181

    Article  Google Scholar 

  34. Wang Y, Guo Q, Sun H, Li Z (2012) An investigation into the impacts of the crucial factors on EVs charging load. IEEE. In: IEEE PES Innovative Smart Grid Technologies, pp 1–4. https://doi.org/10.1109/isgt-asia.2012.6303111

  35. Wang J, Tang J, Xu Z, Wang Y, Xue G, Zhang X, Yang D (2017) Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach. IEEE. In: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, pp 1–9 (2017). https://doi.org/10.1109/infocom.2017.8057090

  36. Werbos PJ (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Netw 1(4):339–356. https://doi.org/10.1016/0893-6080(88)90007-x

    Article  Google Scholar 

  37. Xie F, Huang M, Zhang W, Li J (2011) Research on electric vehicle charging station load forecasting. IEEE. In: 2011 International Conference on Advanced Power System Automation and Protection, pp 2055–2060. https://doi.org/10.1109/apap.2011.6180772

  38. Xingjian S, Chen Z, Wang H, Yeung DY, Wong WK, Woo Wc (2015) Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, pp 802–810

  39. Xydas ES, Marmaras CE, Cipcigan LM, Hassan AS, Jenkins N (2013) Forecasting electric vehicle charging demand using support vector machines. IEEE. In: 2013 48th International Universities’ Power Engineering Conference (UPEC), pp 1–6. https://doi.org/10.1109/upec.2013.6714942

  40. Yeung JFKA, Kai Wei Z, Chan KY, Lau HYK, Yiu KFC (2019) Jump detection in financial time series using machine learning algorithms. Soft Comput 24(3):1789–1801. https://doi.org/10.1007/s00500-019-04006-2

    Article  Google Scholar 

  41. Zachary T (2019) Now Is a Critical Stage for China’s New Energy Vehicles. https://thediplomat.com/2019/04/now-is-a-critical-stage-for-chinas-new-energy-vehicles/. Accessed: 2022-02-21

  42. Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147–166. https://doi.org/10.1016/j.artint.2018.03.002

    Article  MathSciNet  MATH  Google Scholar 

  43. Zhu L, Laptev N (2017) Deep and Confident Prediction for Time Series at Uber. IEEE. 2017 IEEE International Conference on Data Mining Workshops (ICDMW) pp 103–110 https://doi.org/10.1109/icdmw.2017.19

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China (No. 2021ZD0112400), and also in part by the National Natural Science Foundation of China under Grant U1811463.

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  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Mekkaoui Djamel Eddine & Yanming Shen

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Correspondence to Yanming Shen.

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Eddine, M.D., Shen, Y. A deep learning based approach for predicting the demand of electric vehicle charge. J Supercomput 78, 14072–14095 (2022). https://doi.org/10.1007/s11227-022-04428-0

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