TY - JOUR
T1 - Short‐term load forecasting at electric vehicle charging sites using a multivariate multi‐step long short‐term memory
T2 - A case study from Finland
AU - Unterluggauer, Tim
AU - Rauma, Kalle
AU - Järventausta, Pertti
AU - Rehtanz, Christian
N1 - Funding Information:
The authors would like to thank IGL Technologies Oy and Parking Energy Ltd for providing the charging data for this work. Kalle Rauma would like to thank the support of the German Federal Ministry of Transport and Digital Infrastructure through the project PuLS – Parken und Laden in der Stadt (03EMF0203 B). Tim Unterluggauer and Pertti Järventausta would like to thank the support of the project Energiaratkaisut.
Publisher Copyright:
© 2021 The Authors. IET Electrical Systems in Transportation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131009645&partnerID=8YFLogxK
U2 - 10.1049/els2.12028
DO - 10.1049/els2.12028
M3 - Article
SN - 2042-9738
VL - 11
SP - 405
EP - 419
JO - IET Electrical Systems in Transportation
JF - IET Electrical Systems in Transportation
IS - 4
ER -