Abstract
The charging of electric cars affects the performance, efficiency, and required capacity of the electric grid especially where a large electric car fleet located close together simultaneously charges off the same local transformer. Therefore, an accurate load forecasting is required for the reliable and efficient operation of a power system. In this study, three deep learning algorithms, including long short term memory, bidirectional long short term memory, and gated recurrent units are employed and compared in forecasting the aggregate load for the charging of a fleet of electric cars. The developed models were trained and tested on a real-world data set that was collected from 1000 electric vehicles across Canada during 2017–2019. The bidirectional long short term memory algorithm possesses the lowest mean absolute error, mean absolute percentage error and root mean square error among the used methods and is best suited for forecasting the load of electric cars fleet.
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Change history
20 September 2021
In the originally published version of chapter 23 the credit line was incorrect. The credit line has been corrected to “© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources 2021”.
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© 2021 Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources
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Mohsenimanesh, A., Entchev, E., Lapouchnian, A., Ribberink, H. (2021). A Comparative Study of Deep Learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2021 Workshops. DEXA 2021. Communications in Computer and Information Science, vol 1479. Springer, Cham. https://doi.org/10.1007/978-3-030-87101-7_23
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DOI: https://doi.org/10.1007/978-3-030-87101-7_23
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