Electric vehicle load forecasting using data mining methods

Xydas, Erotokritos, Marmaras, Charalampos, Cipcigan, Liana Mirela ORCID: https://orcid.org/0000-0002-5015-3334, Hassan, Asghar and Jenkins, Nicholas ORCID: https://orcid.org/0000-0003-3082-6260 2013. Electric vehicle load forecasting using data mining methods. Presented at: IET Hybrid and Electric Vehicles Conference (HEVC 2013), London, UK, 6-7 November 2013. Proceedings: IET Hybrid and Electric Vehicles Conference (HEVC 2013). Stevenage: IET, 10.1049/cp.2013.1914

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Abstract

The continuous growth and evolve of vehicle electrification causes the electric power systems to confront new challenges, since the load profile changes, and new parameters are being set. With the number of EVs gradually rising, problems may occur in technical characteristics of the network, like bus voltages and line congestion [1]. Therefore, it is necessary to develop EV management systems so as to prevent such phenomena. The effectiveness of such systems is heavily depended on the early knowledge of future demand. This knowledge can be provided by accurate EV load forecasting techniques. In this paper, the use of various data mining methods is examined and their performance in EV load forecasting is evaluated.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	IET
ISBN:	9781849197762
Funders:	EPSRC
Last Modified:	25 Oct 2022 08:56
URI:	https://orca.cardiff.ac.uk/id/eprint/56493

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