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Prediction of charging demand of electric city buses of Helsinki, Finland by random forest

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Deb, Sanchari and Gao, Xiao-Zhi (2022) Prediction of charging demand of electric city buses of Helsinki, Finland by random forest. Energies, 15 (10). 3679. doi:10.3390/en15103679 ISSN 1996-1073.

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Official URL: http://dx.doi.org/10.3390/en15103679

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Abstract

Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme.

Item Type: Journal Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Buses, Electric -- Finland, Urban transportation -- Finland, Battery charging stations (Electric vehicles), Electric vehicles -- Power supply -- Finland, Electric automobiles -- Power supply -- Finland, Electric discharges, Smart power grids
Journal or Publication Title: Energies
Publisher: M.D.P.I.A.G.
ISSN: 1996-1073
Official Date: 17 May 2022
Dates:
DateEvent
17 May 2022Published
25 February 2022Accepted
14 January 2022Submitted
Volume: 15
Number: 10
Number of Pages: 18
Article Number: 3679
DOI: 10.3390/en15103679
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 28 June 2022
Date of first compliant Open Access: 28 June 2022

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