Latest revision as of 11:31, 15 February 2021

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%.

Document type: Article

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The different versions of the original document can be found in:

• https://www.mdpi.com/1996-1073/13/18/4893/pdf under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/1996-1073/13/18/4893,

https://doaj.org/toc/1996-1073 under the license cc-by

• https://www.mdpi.com/1996-1073/13/18/4893,

https://www.mdpi.com/1996-1073/13/18/4893/pdf,

https://academic.microsoft.com/#/detail/3087545070

• https://www.mdpi.com/1996-1073/13/18/4893/pdf,

http://dx.doi.org/10.3390/en13184893

under the license https://creativecommons.org/licenses/by/4.0/