用深度多分位TCN模型预测电动车充电负荷，支持跨站点迁移学习。
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network
- 基于多分位数TCN网络，结合归纳迁移学习提升泛化能力。
- 在JPL站点实现93.62%的预测区间覆盖率，较XGBoost提升28.93%。
- 仅用两周数据即在NREL站点达成96.88%覆盖率，适合数据稀缺场景。
车辆电动化是减少化石燃料使用、降低环境污染的有效途径。不同交通模式（空、水、陆）的电动汽车类型多样，用户群体（通勤者、商业或家庭用户、司机）也各异，其充电基础设施（公共、私人、家用、工作场所）使用时间与行为模式差异大，导致充电需求高度随机。准确刻画并预测多样化电动车使用场景下的充电负荷对防止电网故障至关重要。现有数据驱动负荷模型多局限于特定场景与地点，难以同时实现跨站点日间负荷预测的知识迁移、小样本训练及低成本部署。本文提出一种基于位置的电动车充电站负荷预测方法，采用深度多分位数时序卷积网络（MQ-TCN），克服传统模型局限。实验基于加州理工学院（Caltech）、喷气推进实验室（JPL）、Office-1和国家可再生能源实验室（NREL）四个站点的数据，涵盖学生、全职/兼职员工、随机访客等多元用户。在JPL站点，所提深度MQ-TCN模型达到93.62%的预测区间覆盖率（PICP），相比XGBoost模型提升28.93%。通过归纳迁移学习（inductive Transfer Learning），该模型仅用两周数据即在NREL站点实现96.88%的PICP，显著提升小样本场景下的预测性能。
原文摘要 · Abstract (English)
Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62\%, our proposed deep MQ-TCN model exhibited a remarkable 28.93\% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88\% PICP score for the load forecasting task at the NREL site using only two weeks of data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。