标题： EV-STLLM：基于多频段和多尺度信息融合的时空大语言模型电动汽车充电预测 显示英文标题

标题： EV-STLLM: Electric vehicle charging forecasting based on spatio-temporal large language models with multi-frequency and multi-scale information fusion

摘要： 随着电动汽车（EV）的普及，准确预测充电需求和车站占用情况对于优化城市能源和电动汽车聚合商的利润至关重要。 该领域的现有方法通常难以捕捉电动汽车充电行为中的复杂时空依赖关系，其有限的模型参数阻碍了它们从大数据集中学习复杂数据分布表示的能力。 为此，我们提出了一种新颖的电动汽车时空大语言模型（EV-STLLM）以实现准确预测。 我们提出的框架分为两个模块。 在数据处理模块中，我们利用变分模态分解（VMD）进行数据去噪，并利用改进的完整集成经验模态分解自适应噪声（ICEEMDAN）进行数据多频分解。 模糊信息粒化（FIG）用于提取多尺度信息。 此外，使用ReliefF进行特征选择以减轻冗余。 在预测模块中，EV-STLLM用于直接实现电动汽车充电和占用预测。 首先，我们通过整合来自区域站点网络的邻接矩阵和时空频率嵌入信息，充分捕捉数据的内在时空特性。 然后，部分冻结图注意力（PFGA）模块被用来保持预训练大模型的序列特征建模能力，同时结合电动汽车领域知识。 使用中国深圳的真实世界数据进行的大量实验表明，与最先进的基准相比，我们提出的框架可以实现更高的准确性和鲁棒性。 显示英文摘要

摘要： With the proliferation of electric vehicles (EVs), accurate charging demand and station occupancy forecasting are critical for optimizing urban energy and the profit of EVs aggregator. Existing approaches in this field usually struggle to capture the complex spatio-temporal dependencies in EV charging behaviors, and their limited model parameters hinder their ability to learn complex data distribution representations from large datasets. To this end, we propose a novel EV spatio-temporal large language model (EV-STLLM) for accurate prediction. Our proposed framework is divided into two modules. In the data processing module, we utilize variational mode decomposition (VMD) for data denoising, and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) for data multi-frequency decomposition. Fuzzy information granulation (FIG) for extracting multi-scale information. Additionally, ReliefF is used for feature selection to mitigate redundancy. In the forecasting module, the EV-STLLM is used to directly achieve EV charging and occupancy forecasting. Firstly, we fully capture the intrinsic spatio-temporal characteristics of the data by integrating adjacency matrices derived from the regional stations network and spatio-temporal-frequency embedding information. Then, the partially frozen graph attention (PFGA) module is utilized to maintain the sequential feature modeling capabilities of the pre-trained large model while incorporating EV domain knowledge. Extensive experiments using real-world data from Shenzhen, China, demonstrate that our proposed framework can achieve superior accuracy and robustness compared to the state-of-the-art benchmarks.