Due to the vast electric vehicle (EV) penetration to distribution grid, charging load forecasting is essential to promote charging station operation and demand-side management.However, the stochastic charging behaviors and associated exogenous factors render future charging load patterns quite volatile and hard to predict. Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates. Specifically, we leverage a denoising diffusion model, which can progressively convert the Gaussian prior to real time-series data by learning a reversal of the diffusion process. Besides, we couple such diffusion model with a cross-attention-based conditioning mechanism to execute conditional generation for possible charging demand profiles. We also propose a task-informed fine-tuning technique to better adapt DiffPLF to the probabilistic time-series forecasting task and acquire more accurate and reliable predicted intervals. Finally, we conduct multiple experiments to validate the superiority of DiffPLF to predict complex temporal patterns of erratic charging load and carry out controllable generation based on certain covariate. Results demonstrate that we can attain a notable rise of 39.58% and 49.87% on MAE and CRPS respectively compared to the conventional method.
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this requirement, accurate forecasting of the charging demand is vital. Short and long-term charging demand forecasting will allow for better optimisation of the power grid and future infrastructure expansions. In this paper, we propose to use publicly available data to forecast the electric vehicle charging demand. To model the complex spatial-temporal correlations between charging stations, we argue that Temporal Graph …
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Data Science & Intelligent Systems (DSIS) Research Group, University of Bonn（波恩大学数据科学与智能系统（DSIS）研究组）
With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our…
Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure
Laboratoire de Mathematiques d’Orsay, Université Paris-Saclay, CNRS, INRIA（奥赛数学实验室，巴黎萨克雷大学，法国国家科学研究中心，法国国家信息与自动化研究所）；Université Paris-Cité（巴黎西岱大学）；Electricité de France R&D（法国电力公司研发部）
Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand
Tsinghua University（清华大学）；State Key Laboratory of Power Systems（电力系统国家重点实验室）；Beijing Deepseek Artificial Intelligence Fundamental Technology Research Co., Ltd.（北京深度求索人工智能基础技术研究有限公司）；Huazhong University of Science and Technology（华中科技大学）；School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（华中科技大学电气与电子工程学院）；The University of Hong Kong（香港大学）
Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations
Shenzhen Power Supply Bureau Co., Ltd.（深圳供电局有限公司）；Electric Power Research Institute, CSG（中国南方电网电力科学研究院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua University（清华大学）
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
College of Electronic and Information Engineering, Southwest University（西南大学电子信息工程学院）；School of Electrical Engineering and Telecommunications, the University of New South Wales（新南威尔士大学电气工程与电信学院）；Department of Electrical Engineering, City University of Hong Kong（香港城市大学电气工程系）；Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电气与电子工程系）
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considerin…
LSTM-Based Forecasting and Analysis of EV Charging Demand in a Dense Urban Campus
Electrical and Electronic Engineering Department, California State University-Sacramento（电气与电子工程系，加州州立大学萨克拉门托分校）；Electrical and Electronic Engineering Department, University of California, Riverside（电气与电子工程系，加州大学河滨分校）；Computer Science Department, California State University, Northridge（计算机科学系，加州州立大学北岭分校）；Computer Engineering Department, California State University, Northridge（计算机工程系，加州州立大学北岭分校）
本文提出一种结合插值、归一化与特征提取的 LSTM 充电负荷预测框架，可在日、周、月尺度上准确预测多地点 EV 充电需求，并支持不同部署场景。
The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of …
University of California, Los Angeles (UCLA)（加州大学洛杉矶分校）
Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as test…
The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs…
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
School of Intelligent Systems Engineering, Sun Yat-Sen University（中山大学智能工程学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
