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Open AccessArticle

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

1
Smart Power Distribution Laboratory, KEPCO Research Institute, Daejeon 34056, Korea
2
Division of Computer Science and Engineering, Kyonggi University, Gyeonggi 16227, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4893; https://doi.org/10.3390/en13184893
Received: 22 August 2020 / Revised: 15 September 2020 / Accepted: 16 September 2020 / Published: 18 September 2020
(This article belongs to the Special Issue Machine Learning and Deep Learning for Energy Systems)
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%. View Full-Text
Keywords: electric vehicles; load forecasting; long short-term memory; missing values; data imputation electric vehicles; load forecasting; long short-term memory; missing values; data imputation
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MDPI and ACS Style

Lee, B.; Lee, H.; Ahn, H. Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation. Energies 2020, 13, 4893. https://doi.org/10.3390/en13184893

AMA Style

Lee B, Lee H, Ahn H. Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation. Energies. 2020; 13(18):4893. https://doi.org/10.3390/en13184893

Chicago/Turabian Style

Lee, Byungsung; Lee, Haesung; Ahn, Hyun. 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation" Energies 13, no. 18: 4893. https://doi.org/10.3390/en13184893

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