Scipediacontent: Scipediacontent moved page Draft Content 769395940 to Ahn et al 2020a

2021-02-15T10:31:12Z

Scipediacontent moved page Draft Content 769395940 to Ahn et al 2020a

Scipediacontent: Created page with " == Abstract == As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distri..."

2021-02-15T10:31:08Z

Created page with " == Abstract == As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distri..."

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== 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

== Full document ==
<pdf>Media:Draft_Content_769395940-beopen872-9908-document.pdf</pdf>

== Original document ==

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

* [https://www.mdpi.com/1996-1073/13/18/4893/pdf 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://www.mdpi.com/1996-1073/13/18/4893],
: [https://doaj.org/toc/1996-1073 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],
: [https://www.mdpi.com/1996-1073/13/18/4893/pdf https://www.mdpi.com/1996-1073/13/18/4893/pdf],
: [https://academic.microsoft.com/#/detail/3087545070 https://academic.microsoft.com/#/detail/3087545070]

* [https://www.mdpi.com/1996-1073/13/18/4893/pdf https://www.mdpi.com/1996-1073/13/18/4893/pdf],
: [http://dx.doi.org/10.3390/en13184893 http://dx.doi.org/10.3390/en13184893]

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

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Ahn et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 769395940 to Ahn et al 2020a

Scipediacontent: Created page with " == Abstract == As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distri..."