TY - JOUR
T1 - LSTM-based forecasting on electric vehicles battery swapping demand
T2 - Addressing infrastructure challenge in Indonesia
AU - Romdlony, Muhammad Zakiyullah
AU - Khayr, Rashad Abul
AU - Muharam, Aam
AU - Priandana, Eka Rakhman
AU - Sasmono, Sudarmono
AU - Rosa, Muhammad Ridho
AU - Purnama, Irwan
AU - Amin,
AU - Fachri, Ridlho Khoirul
N1 - Publisher Copyright:
© 2023 National Research and Innovation Agency.
PY - 2023
Y1 - 2023
N2 - This article aims to design a model for forecasting the number of vehicles arriving at the battery swap station (BSS). In our case, we study the relevance of the proposed approach given the rapid increase in electric vehicle users in Indonesia. Due to the vehicle electrification program from the government of Indonesia and the lack of supporting infrastructure, forecasting battery swap demands is very important for charging schedules. Forecasting the number of vehicles is done using machine learning with the long short-term memory (LSTM) method. The method is used to predict sequential data because of its ability to review previous data in addition to the current input. The result of the forecasting using the LSTM method yields a prediction score using the root-mean-square error (RMSE) of 2.3079 × 10−6. The forecasted data can be combined with the battery charging model to acquire predicted hourly battery availability that can be processed further for optimization and scheduling.
AB - This article aims to design a model for forecasting the number of vehicles arriving at the battery swap station (BSS). In our case, we study the relevance of the proposed approach given the rapid increase in electric vehicle users in Indonesia. Due to the vehicle electrification program from the government of Indonesia and the lack of supporting infrastructure, forecasting battery swap demands is very important for charging schedules. Forecasting the number of vehicles is done using machine learning with the long short-term memory (LSTM) method. The method is used to predict sequential data because of its ability to review previous data in addition to the current input. The result of the forecasting using the LSTM method yields a prediction score using the root-mean-square error (RMSE) of 2.3079 × 10−6. The forecasted data can be combined with the battery charging model to acquire predicted hourly battery availability that can be processed further for optimization and scheduling.
KW - battery swap station (BSS)
KW - demand forecasting
KW - long short-term memory (LSTM)
UR - http://www.scopus.com/inward/record.url?scp=85167700170&partnerID=8YFLogxK
U2 - 10.14203/j.mev.2023.v14.72-79
DO - 10.14203/j.mev.2023.v14.72-79
M3 - Article
AN - SCOPUS:85167700170
SN - 2087-3379
VL - 14
SP - 72
EP - 79
JO - Journal of Mechatronics, Electrical Power, and Vehicular Technology
JF - Journal of Mechatronics, Electrical Power, and Vehicular Technology
IS - 1
ER -