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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: Syahrizal, Salleh, Roslinazairimah, Zakaria, Siti Roslindar, Yaziz
Format: Book Chapter
Language:English
English
English
Published: Springer Cham 2024
Subjects:
QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
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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spelling my.ump.umpir.425042024-09-05T00:49:24Z http://umpir.ump.edu.my/id/eprint/42504/ Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition Syahrizal, Salleh Roslinazairimah, Zakaria Siti Roslindar, Yaziz QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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 events at 1-min intervals. In an effort to enhance prediction accuracy, the transformed BEV charging data was subjected to decomposition into its trend, seasonality, and residual components using the Seasonal-Trend decomposition using Loess (STL) procedure. Acknowledging the nonlinear, dynamic, and noisy characteristics inherent in BEV charging behavior, the Long Short-Term Memory (LSTM) network was chosen to model electricity demand arising from multiple concurrent BEV charging events. In the model optimization process, hyperparameter tuning focused on adjusting the number of epochs at intervals of 1, 10, 20, 30, 40, and 50. The prediction values for the STL-LSTM decomposed trend, seasonality, and residual components achieved their lowest Mean Absolute Percentage Error (MAPE) values against decomposed testing data at 0.03%, 4.37%, and 27.11%, respectively. The corresponding Root Mean Squared Error (RMSE) values were 0.01, 0.08, and 0.49. Upon reconstruction and comparison against observed testing data, the resulting lowest MAPE occurred at epochs 10, 30, and 40 for trend, seasonality, and residuals, respectively, with values of 1.21% and RMSE 0.51. This is marginally lower than the forecast using the LSTM model on observed data, which recorded a MAPE of 1.38% at RMSE 0.51. The findings underscore the suitability of the STL-LSTM model, tailored for 1-min resolution electricity demand, for electric utility companies aiming to forecast very short-term loads. Springer Cham 2024-07-30 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42504/1/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/42504/2/Battery%20electric%20vehicle%20charging%20load%20forecasting%20using%20LSTM%20on%20STL%20trend.pdf pdf en http://umpir.ump.edu.my/id/eprint/42504/3/Recent%20Advances%20on%20Soft%20Computing%20and%20Data%20Mining.pdf Syahrizal, Salleh and Roslinazairimah, Zakaria and Siti Roslindar, Yaziz (2024) Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition. In: Recent Advances on Soft Computing and Data Mining. Lecture Notes in Networks and Systems, 1078 . Springer Cham, Switzerland, pp. 327-336. ISBN 978-3-031-66965-1 https://doi.org/10.1007/978-3-031-66965-1_32 https://doi.org/10.1007/978-3-031-66965-1
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
English
topic QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
TK Electrical engineering. Electronics Nuclear engineering
Syahrizal, Salleh
Roslinazairimah, Zakaria
Siti Roslindar, Yaziz
Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
description 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 events at 1-min intervals. In an effort to enhance prediction accuracy, the transformed BEV charging data was subjected to decomposition into its trend, seasonality, and residual components using the Seasonal-Trend decomposition using Loess (STL) procedure. Acknowledging the nonlinear, dynamic, and noisy characteristics inherent in BEV charging behavior, the Long Short-Term Memory (LSTM) network was chosen to model electricity demand arising from multiple concurrent BEV charging events. In the model optimization process, hyperparameter tuning focused on adjusting the number of epochs at intervals of 1, 10, 20, 30, 40, and 50. The prediction values for the STL-LSTM decomposed trend, seasonality, and residual components achieved their lowest Mean Absolute Percentage Error (MAPE) values against decomposed testing data at 0.03%, 4.37%, and 27.11%, respectively. The corresponding Root Mean Squared Error (RMSE) values were 0.01, 0.08, and 0.49. Upon reconstruction and comparison against observed testing data, the resulting lowest MAPE occurred at epochs 10, 30, and 40 for trend, seasonality, and residuals, respectively, with values of 1.21% and RMSE 0.51. This is marginally lower than the forecast using the LSTM model on observed data, which recorded a MAPE of 1.38% at RMSE 0.51. The findings underscore the suitability of the STL-LSTM model, tailored for 1-min resolution electricity demand, for electric utility companies aiming to forecast very short-term loads.
format Book Chapter
author Syahrizal, Salleh
Roslinazairimah, Zakaria
Siti Roslindar, Yaziz
author_facet Syahrizal, Salleh
Roslinazairimah, Zakaria
Siti Roslindar, Yaziz
author_sort Syahrizal, Salleh
title Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
title_short Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
title_full Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
title_fullStr Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
title_full_unstemmed Battery electric vehicle charging load forecasting using LSTM on STL trend, seasonality, and residual decomposition
title_sort battery electric vehicle charging load forecasting using lstm on stl trend, seasonality, and residual decomposition
publisher Springer Cham
publishDate 2024
url 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
_version_ 1822924652245155840
score 13.232414

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