摘要
近年来,电动车换电服务凭借其快速便捷的补能优势得到迅速推广。精确的用户换电需求预测是提高换电平台运营效率的关键。然而,在换电站新部署城市中,由于历史数据的缺乏,传统预测模型往往训练不足,导致预测精度下降。为解决该难题,提出一种基于去噪扩散模型增强的换电需求数据生成算法。通过生成与真实换电需求数据分布一致的合成样本,对训练数据进行有效扩充,从而显著提升模型的预测精度。具体地,首先使用模拟位编码在连续空间中表示混合类型换电需求数据,使其能被扩散模型处理。进一步地,设计一个基于交叉注意力机制的条件去噪网络,并使用站点信息引导生成高质量换电需求数据。最后,基于成都市40个换电站一个月的真实换电数据集对所提算法进行性能评估。实验结果表明,与直接使用原训练数据训练相比,使用所提算法的生成数据与原训练数据结合后的换电需求预测在MAE,RMSE和MAPE误差指标上分别降低了9.29%,8.56%和8.23%。
In recent years,electric vehicle battery swap services have rapidly gained popularity due to their fast and convenient energy replenishment capabilities.Accurate prediction of user battery swap demand is crucial for optimizing the operational efficiency of battery swap platforms.However,in cities with newly deployed battery swap stations,conventional prediction models often suffer from insufficient training due to the lack of historical data,resulting in degraded prediction accuracy.To address this challenge,this paper proposes a denoising diffusion model-enhanced algorithm for generating battery swap demand data.By synthesizing samples that preserve the statistical distribution of real-world battery swap demand data,the proposed approach effectively augments the training dataset,thereby significantly improving the model’s prediction accuracy.Specifically,it firstly employs analog bit encoding to represent mixed-type battery swap demand data in continuous space,enabling it to be processed by the diffusion model.Furthermore,it designs a conditional denoising network incorporating the cross-attention mechanism,utilizing station information to guide the generation of high-quality battery swap demand data.Finally,the proposed algorithm is evaluated on a real-world battery swap dataset collected from 40 battery swap stations in Chengdu over a one-month period.Experimental results demonstrate that combining the generated data from the proposed algorithm with the original training data reduces the MAE,RMSE,and MAPE of battery swap demand prediction by 9.29%,8.56%,and 8.23%,respectively,compared to using the original training data alone.
作者
刘德华
喻赛萱
乔金兰
黄河清
程文辉
LIU Dehua;YU Saixuan;QIAO Jinlan;HUANG Heqing;CHENG Wenhui(College of Computer Science,Chongqing University,Chongqing 400044,China;College of Information,Sichuan Vocational College of Finance and Economics,Chengdu 610101,China;Department of Logistics Command,Engineering University of the Joint Logistics Support Force,Chongqing 401331,China;College of Chongqing Technology and Business,Chongqing Open University,Chongqing 400053,China)
出处
《计算机科学》
北大核心
2026年第4期163-172,共10页
Computer Science
基金
重庆市教委重点科技项目(KJZD-K202404002)
合川区科技项目(HCKJ-2024-112)。
关键词
电动车换电服务
换电需求预测
换电需求数据生成
去噪扩散模型
交叉注意力机制
Electric vehicle battery swap service
Battery swap demand prediction
Battery swap demand data generation
Denoi-sing diffusion model
Cross-attention mechanism
