Scipediacontent: Scipediacontent moved page Draft Content 227263604 to Yang et al 2019b

2021-02-15T09:33:46Z

Scipediacontent moved page Draft Content 227263604 to Yang et al 2019b

Scipediacontent: Created page with " == Abstract == Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to t..."

2021-02-15T09:33:43Z

Created page with " == Abstract == Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to t..."

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== Abstract ==

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_227263604-beopen754-8488-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/app9091723 http://dx.doi.org/10.3390/app9091723] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.3390/app9091723 http://dx.doi.org/10.3390/app9091723] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/2076-3417/9/9/1723/pdf https://www.mdpi.com/2076-3417/9/9/1723/pdf] under the license https://creativecommons.org/licenses/by/4.0

* [https://doi.org/10.3390/app9091723 https://doi.org/10.3390/app9091723],
: [https://academic.microsoft.com/#/detail/2941906448 https://academic.microsoft.com/#/detail/2941906448] under the license cc-by

* [https://www.mdpi.com/2076-3417/9/9/1723 https://www.mdpi.com/2076-3417/9/9/1723],
: [https://doaj.org/toc/2076-3417 https://doaj.org/toc/2076-3417]

* [https://www.mdpi.com/2076-3417/9/9/1723/pdf https://www.mdpi.com/2076-3417/9/9/1723/pdf],
: [http://dx.doi.org/10.3390/app9091723 http://dx.doi.org/10.3390/app9091723]

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

← Older revision	Revision as of 09:33, 15 February 2021
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Yang et al 2019b - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 227263604 to Yang et al 2019b

Scipediacontent: Created page with " == Abstract == Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to t..."