Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland
Abstract This study assesses the performance of a multivariate multi‐step charging load prediction approach based on the long short‐term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging si...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Electrical Systems in Transportation |
| Online Access: | https://doi.org/10.1049/els2.12028 |
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| _version_ | 1832559613476601856 |
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| author | Tim Unterluggauer Kalle Rauma Pertti Järventausta Christian Rehtanz |
| author_facet | Tim Unterluggauer Kalle Rauma Pertti Järventausta Christian Rehtanz |
| author_sort | Tim Unterluggauer |
| collection | DOAJ |
| description | Abstract This study assesses the performance of a multivariate multi‐step charging load prediction approach based on the long short‐term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15‐min resolution for each type of charging site. The second contribution of the work is the extended short‐term forecast horizon. A multi‐step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4‐time step prediction yields considerably superior results compared with the 96‐time step forecast. |
| format | Article |
| id | doaj-art-3293c513ad3e450598f6f87aa7cfa374 |
| institution | Kabale University |
| issn | 2042-9738 2042-9746 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj-art-3293c513ad3e450598f6f87aa7cfa3742025-02-03T01:29:38ZengWileyIET Electrical Systems in Transportation2042-97382042-97462021-12-0111440541910.1049/els2.12028Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from FinlandTim Unterluggauer0Kalle Rauma1Pertti Järventausta2Christian Rehtanz3Unit of Electrical Engineering Tampere University Tampere FinlandInstitute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund GermanyUnit of Electrical Engineering Tampere University Tampere FinlandInstitute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund GermanyAbstract This study assesses the performance of a multivariate multi‐step charging load prediction approach based on the long short‐term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15‐min resolution for each type of charging site. The second contribution of the work is the extended short‐term forecast horizon. A multi‐step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4‐time step prediction yields considerably superior results compared with the 96‐time step forecast.https://doi.org/10.1049/els2.12028 |
| spellingShingle | Tim Unterluggauer Kalle Rauma Pertti Järventausta Christian Rehtanz Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland IET Electrical Systems in Transportation |
| title | Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland |
| title_full | Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland |
| title_fullStr | Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland |
| title_full_unstemmed | Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland |
| title_short | Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory: A case study from Finland |
| title_sort | short term load forecasting at electric vehicle charging sites using a multivariate multi step long short term memory a case study from finland |
| url | https://doi.org/10.1049/els2.12028 |
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