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Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Author

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  • Byungsung Lee

    (Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, Korea)

  • Haesung Lee

    (Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, Korea)

  • Hyun Ahn

    (Division of Computer Science and Engineering, Kyonggi University, Gyeonggi 16227, Korea)

Abstract

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4893-:d:415476
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    References listed on IDEAS

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    1. Jaehyun Lee & Eunjung Lee & Jinho Kim, 2020. "Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    3. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    4. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
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    Cited by:

    1. 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.
    2. 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.

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