Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
To overcome the challenge of limited high-resolution Battery Electric Vehicle (BEV) charging data, a unique feature engineering technique was implemented. The start-stop electricity charging data from the My Electric Avenue project underwent transformation into a count of concurrent active charging...
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| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | English English English |
| Published: |
Springer Cham
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42504/1/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend_ABST.pdf http://umpir.ump.edu.my/id/eprint/42504/2/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend.pdf http://umpir.ump.edu.my/id/eprint/42504/3/Recent%20Advances%20on%20Soft%20Computing%20and%20Data%20Mining.pdf http://umpir.ump.edu.my/id/eprint/42504/ https://doi.org/10.1007/978-3-031-66965-1_32 https://doi.org/10.1007/978-3-031-66965-1 |
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Internet
http://umpir.ump.edu.my/id/eprint/42504/1/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend_ABST.pdfhttp://umpir.ump.edu.my/id/eprint/42504/2/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend.pdf
http://umpir.ump.edu.my/id/eprint/42504/3/Recent%20Advances%20on%20Soft%20Computing%20and%20Data%20Mining.pdf
http://umpir.ump.edu.my/id/eprint/42504/
https://doi.org/10.1007/978-3-031-66965-1_32
https://doi.org/10.1007/978-3-031-66965-1