The rapid expansion of electric vehicles (EVs) has rendered the load
forecasting of electric vehicle charging stations (EVCS) increasingly critical.
The primary challenge in achieving precise load forecasting for EVCS lies in
accounting for the nonlinear of charging behaviors, the spatial interactions
among different stations, and the intricate temporal variations in usage
patterns. To address these challenges, we propose a Multiscale Spatio-Temporal
Enhanced Model (MSTEM) for effective load forecasting at EVCS. MSTEM
incorporates a multiscale graph neural network to discern hierarchical
nonlinear temporal dependencies across various time scales. Besides, it also
integrates a recurrent learning component and a residual fusion mechanism,
enhancing its capability to accurately capture spatial and temporal variations
in charging patterns. The effectiveness of the proposed MSTEM has been
validated through comparative analysis with six baseline models using three
evaluation metrics. The case studies utilize real-world datasets for both fast
and slow charging loads at EVCS in Perth, UK. The experimental results
demonstrate the superiority of MSTEM in short-term continuous load forecasting
for EVCS.

通过使用多尺度时空增强模型（MSTEM），本研究在电动汽车充电站（EVCS）的精确负载预测方面提出了一种解决方案，该模型通过多尺度图神经网络以及回归学习组件和残差融合机制的整合，能够准确捕捉电动汽车充电行为的非线性、不同站点之间的空间相互作用以及使用模式的复杂时间变化，并在实际数据集上验证了其优越性。

电动汽车充电站多尺度时空增强的短期负荷预测

Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of  Electric Vehicle Charging Stations

Effective energy management of electric vehicle (EV) charging stations is
critical to supporting the transport sector's sustainable energy transition.
This paper addresses the EV charging coordination by considering
vehicle-to-vehicle (V2V) energy exchange as the flexibility to harness in EV
charging stations. Moreover, this paper takes into account EV user experiences,
such as charging satisfaction and fairness. We propose a Multi-Agent
Reinforcement Learning (MARL) approach to coordinate EV charging with V2V
energy exchange while considering uncertainties in the EV arrival time, energy
price, and solar energy generation. The exploration capability of MARL is
enhanced by introducing parameter noise into MARL's neural network models.
Experimental results demonstrate the superior performance and scalability of
our proposed method compared to traditional optimization baselines. The
decentralized execution of the algorithm enables it to effectively deal with
partial system faults in the charging station.

该研究论文提出了一种多智能体强化学习的方法，以协调具有车对车能量交换的电动车充电，并考虑了电动车用户体验和去中心化执行的能力。实验结果表明，该方法相比传统优化算法具有卓越的性能和可扩展性。