Performance Analysis of LSTMs for Daily Individual EV Charging Behavior Prediction
In this paper, we evaluate and analyze the performance of long short-term memory networks (LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day. The charging behavior consists of the charging duration level within a certain upper and lower range, the time slots...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2c1b90ffa9264d209a2757be23306aea |
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| Sumario: | In this paper, we evaluate and analyze the performance of long short-term memory networks (LSTMs) for individual electric vehicle (EV) charging behavior prediction over the next day. The charging behavior consists of the charging duration level within a certain upper and lower range, the time slots in which charging will take place, the number of times charging will take place in each time slot, and whether the next day will be a charging day or not. Unlike existing work, we evaluate the behavior prediction performance for increasing resolutions of charging duration levels and charging time slots, using varying lengths of training data. The performance of the proposed approach is validated using real EV charging data, and comparison with other machine learning methods shows its generally superior prediction accuracy for all resolutions. We show that the best performance is achieved when around 8–10 months of data are used as training data. It is also shown that although the performance of the LSTMs degrades with increasing resolution, the performance for charging time slot prediction is affected less compared to that for charging duration prediction. We further propose, analyze and evaluate a new technique that improves the charging duration prediction performance. |
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