Predicting Electric Vehicle Charging Station Usage from Historical Data

根据历史数据预测电动汽车充电站的使用情况

基本信息

  • 批准号:
    543736-2019
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Engage Grants Program
  • 财政年份:
    2019
  • 资助国家:
    加拿大
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

It is increasingly important that utilities have a precise view of how much energy their grid will need to provide to support the rapid growth of electric vehicles and the charging infrastructure. Meanwhile, electric vehicle users want to know where best to charge their vehicles.Mogile Technologies operates ChargeHub, a platform that, amongst other things, helps consumers find charging stations on the go. In support of this product, they aggregate data on charging station usage; if they can use this data to predict usage, it will dramatically improve and expand the services they can provide to both utilities and consumers alike.The objective of this project is to identify machine learning models that best predict future charging station usage. We will explore a variety of approaches, from regression to deep learning; we will work with a variety of input features, including those the characterize past charging events (e.g., per charge energy), and, time permitting, additional variables expected improve accuracy (e.g., weather conditions). As domain experts, Mogile will work with us closely to identify the appropriate strategy for constructing a set of training data. As machine learning experts, we will build and evaluate models that, given (a) training data, and (b) past usage, (c) predicts usage over the next seven days, and (d) associated prediction confidence. This work will delivered in the form of a prototype API, and documented in the form of a deliverable report.
越来越重要的是,公用事业公司必须准确了解他们的电网需要提供多少能量来支持电动汽车和充电基础设施的快速增长。与此同时,电动汽车用户想知道在哪里最适合为他们的汽车充电。Mogile Technologies运营着ChargeHub平台,该平台除其他外,还帮助消费者在旅途中找到充电站。为了支持该产品,他们收集了充电站使用情况的数据;如果他们可以使用这些数据来预测使用情况,这将大大改善和扩展他们可以为公用事业公司和消费者提供的服务。该项目的目标是确定最能预测未来充电站使用情况的机器学习模型。我们将探索各种方法,从回归到深度学习;我们将使用各种输入特征,包括那些表征过去充电事件的特征(例如,每充电能量),并且,时间允许的话,预期的附加变量提高了准确度(例如,天气条件)。作为领域专家,Mogile将与我们密切合作,确定构建一组训练数据的适当策略。作为机器学习专家,我们将构建和评估模型,给定(a)训练数据和(B)过去的使用情况,(c)预测未来七天的使用情况,以及(d)相关的预测置信度。这项工作将以原型API的形式交付,并以交付报告的形式记录。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Meyer, Brett其他文献

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis.
  • DOI:
    10.1109/tnsre.2023.3271601
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    VanDyk, Tyler;Meyer, Brett;DePetrillo, Paolo;Donahue, Nicole;O'Leary, Aisling;Fox, Sam;Cheney, Nick;Ceruolo, Melissa;Solomon, Andrew J.;McGinnis, Ryan S.
  • 通讯作者:
    McGinnis, Ryan S.
Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis.
  • DOI:
    10.1016/j.gaitpost.2022.02.016
  • 发表时间:
    2022-05
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    Tulipani, Lindsey J.;Meyer, Brett;Allen, Dakota;Solomon, Andrew J.;McGinnis, Ryan S.
  • 通讯作者:
    McGinnis, Ryan S.

Meyer, Brett的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Meyer, Brett', 18)}}的其他基金

Neural-Network-Aided Engineering: New Frontiers in Automation
神经网络辅助工程:自动化新领域
  • 批准号:
    RGPIN-2018-05668
  • 财政年份:
    2022
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Neural-Network-Aided Engineering: New Frontiers in Automation
神经网络辅助工程:自动化新领域
  • 批准号:
    RGPIN-2018-05668
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Neural-Network-Aided Engineering: New Frontiers in Automation
神经网络辅助工程:自动化新领域
  • 批准号:
    RGPIN-2018-05668
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Neural-Network-Aided Engineering: New Frontiers in Automation
神经网络辅助工程:自动化新领域
  • 批准号:
    RGPIN-2018-05668
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Neural-Network-Aided Engineering: New Frontiers in Automation
神经网络辅助工程:自动化新领域
  • 批准号:
    RGPIN-2018-05668
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Architecture and Automation Techniques for Resilient Computer Systems
弹性计算机系统的体系结构和自动化技术
  • 批准号:
    418639-2012
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
VECOS: Comprehensive Vulnerability Analysis and Mitigation Development Framework for Vehicular Communication Systems
VECOS:车辆通信系统综合漏洞分析和缓解开发框架
  • 批准号:
    507155-2016
  • 财政年份:
    2016
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Grants Program
Architecture and Automation Techniques for Resilient Computer Systems
弹性计算机系统的体系结构和自动化技术
  • 批准号:
    418639-2012
  • 财政年份:
    2015
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Architecture and Automation Techniques for Resilient Computer Systems
弹性计算机系统的体系结构和自动化技术
  • 批准号:
    418639-2012
  • 财政年份:
    2014
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
VACE: Vulnerability Assessment and Cost Estimation framework for cost-effective reliable system design
VACE:用于经济有效的可靠系统设计的漏洞评估和成本估算框架
  • 批准号:
    460795-2013
  • 财政年份:
    2013
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Engage Plus Grants Program

相似国自然基金

Probing matter-antimatter asymmetry with the muon electric dipole moment
  • 批准号:
  • 批准年份:
    2020
  • 资助金额:
    30 万元
  • 项目类别:

相似海外基金

Electric Vehicle Education for New Jersey (EVE-NJ)
新泽西州电动汽车教育 (EVE-NJ)
  • 批准号:
    2400825
  • 财政年份:
    2024
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Standard Grant
Structural statistical learning of heterogeneous preferences for smart energy choices with a case study on coordinated electric vehicle charging
智能能源选择异构偏好的结构统计学习以及协调电动汽车充电的案例研究
  • 批准号:
    2342215
  • 财政年份:
    2024
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Continuing Grant
Assessing the Coordination of Electric Vehicle Adoption on Urban Energy Transition: A Geospatial Machine Learning Framework
评估电动汽车采用对城市能源转型的协调:地理空间机器学习框架
  • 批准号:
    24K20973
  • 财政年份:
    2024
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
RII Track-4:NSF: Spatiotemporal Modeling of Lithium-ion Battery Packs for Electric Vehicle Battery Management Systems
RII Track-4:NSF:电动汽车电池管理系统锂离子电池组的时空建模
  • 批准号:
    2327409
  • 财政年份:
    2024
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Standard Grant
Building Australia's Electric Vehicle Fast Charging Infrastructure
建设澳大利亚电动汽车快速充电基础设施
  • 批准号:
    LP210200473
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Linkage Projects
Electric Vehicle Advanced Inverter Technology (EleVAIT)
电动汽车先进逆变器技术 (EleVAIT)
  • 批准号:
    10031208
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    BEIS-Funded Programmes
Accelerating Advanced Electric Vehicle Technician Education While Increasing the Recruitment and Retention of Women
加快高级电动汽车技师教育,同时增加女性的招聘和保留
  • 批准号:
    2300497
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Standard Grant
Towards an Electric Vehicle Service Technology Certificate: Building Career Pathways by Implementing a Hybrid Curriculum in Electric Vehicle Technology
迈向电动汽车服务技术证书:通过实施电动汽车技术混合课程构建职业道路
  • 批准号:
    2300950
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Standard Grant
Development of new, innovative technologies for electric vehicle battery recycling & low carbon minerals recovery in the Tees Valley.
开发电动汽车电池回收的创新技术
  • 批准号:
    10075111
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Launchpad
Performance Guarantees for Electric Vehicle Fast Charging Station Management
电动汽车快速充电站管理的绩效保证
  • 批准号:
    2312196
  • 财政年份:
    2023
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了