Prediction of Electric Vehicles Charging Load Using Long Short-Term Memory Model
Prediction of Electric Vehicles Charging Load Using Long Short-Term Memory Model
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使用长短期记忆模型预测电动汽车充电负荷
DOI：
10.1061/9780784483787.006
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
and Yu-Fang Jin
中科院分区：
文献类型：
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作者：
Eugenia Cadete;Caiwen Ding;Mimi Xie;Sara Ahmed;and Yu-Fang Jin
电动汽车（EV）的数量在过去的几十年中显着增加，由于其优点，包括减少排放和提高能源效率。然而，采用电动汽车可能会导致电网过载，并降低配电系统的电能质量。这也要求增加电动汽车充电站的数量。为了在2030年前以有限的充电站满足1500万辆电动汽车的充电需求，预测充电需求并重新分配充电资源是新兴的需求。在这项研究中，长短期记忆（LSTM）和自回归和移动平均模型（阿尔马）模型应用于预测充电负荷的时间剖面从3个充电站。预测精度被用来评估模型的性能。与阿尔马模型相比，LSTM模型表现出显着的性能改进。研究结果为有效管理充电资源奠定了基础。
The number of electric vehicles (EV) has increased significantly in the past decades due to its advantages including emission reduction and improved energy efficiency. However, the adoption of EV could lead to overloading the grid and degrading the power quality of the distribution system. It also demands an increase in the number of EV charging stations. To meet the charging needs of 15 million EVs by the year 2030 with limited charging stations, prediction of charging needs, and reallocating charging resources are in emerging needs. In this study, long short-term memory (LSTM) and autoregressive and moving average models (ARMA) models were applied to predict charging loads with temporal profiles from 3 charging stations. Prediction accuracy was applied to evaluate the performance of the models. The LSTM models demonstrated a significant performance improvement compared to ARMA models. The results from this study lay a foundation to efficiently manage charge resources.