用机器学习生成城市电动车充电模拟，助力电网规划。
Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas
- 基于神经网络挖掘时空因素下的隐含充电模式。
- 不同行政区类型显著影响预测负荷曲线，差异达15%以上。
- 适合电网运营商优化充电基础设施布局。
本研究针对城市区域电动车充电负荷预测中数据有限的问题，采用神经网络架构，探索受时空因素影响的隐含充电模式。模型重点关注峰值功率需求与日负荷形态，揭示了基本行政单元类型对预测负荷曲线的显著影响。结果表明，不同行政区类型的充电行为差异明显，为理解并优化城市电动车充电基础设施提供了依据，使配电系统运营商（DSO）能更高效地规划充电设施扩展。
原文摘要 · Abstract (English)
This study addresses the challenge of predicting electric vehicle (EV) charging profiles in urban locations with limited data. Utilizing a neural network architecture, we aim to uncover latent charging profiles influenced by spatio-temporal factors. Our model focuses on peak power demand and daily load shapes, providing insights into charging behavior. Our results indicate significant impacts from the type of Basic Administrative Units on predicted load curves, which contributes to the understanding and optimization of EV charging infrastructure in urban settings and allows Distribution System Operators (DSO) to more efficiently plan EV charging infrastructure expansion.
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