ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application

Yitong Shang; Hang Yu; Ziyun Shao; Linni Jian

doi:10.1109/EI252483.2021.9713203

ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application

Yitong Shang
, Hang Yu
, Ziyun Shao
, Linni Jian^*

^*此作品的通讯作者

Harbin Institute of Technology Shenzhen
Southern University of Science and Technology
Guangzhou University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

Vehicle-to-grid (V2G) is an emerging technology for grid stability and environmental protection. However, predicting the unknown data accurately in V2G optimization is difficult. And utilizing the machine learning and big data method for V2G operation becomes a feasible method. Therefore, a vehicle-to-grid dataset for commercial center and its machine learning application are conducted in this paper. Firstly, three categories of datasets, i.e., conventional load data, PV output data and PEV charging data, are selected as the trend of power grid operation. Secondly, a V2G scenario of commercial center is designed, and the maximum values of three parameters were calculated. Next, both the centralized method and the distributed method are used for data annotation. In case study, the dataset is applied through a variety of machine learning methods. The results show that the accuracy of V2G prediction is up to 94%.

源语言	英语
主期刊名	5th IEEE Conference on Energy Internet and Energy System Integration
主期刊副标题	Energy Internet for Carbon Neutrality, EI2 2021
出版商	Institute of Electrical and Electronics Engineers Inc.
页	3246-3250
页数	5
ISBN（电子版）	9781665434256
DOI	https://doi.org/10.1109/EI252483.2021.9713203
出版状态	已出版 - 2021
已对外发布	是
活动	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 - Taiyuan, 中国期限: 22 10月 2021 → 25 10月 2021

丛书

姓名	5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

会议

会议	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
国家/地区	中国
市	Taiyuan
时期	22/10/21 → 25/10/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/EI252483.2021.9713203

其它文件与链接

链接到 Scopus 的出版物

引用此

Shang, Y., Yu, H., Shao, Z., & Jian, L. (2021). ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. 在 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021 (页码 3246-3250). (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI252483.2021.9713203

Shang, Yitong ; Yu, Hang ; Shao, Ziyun 等. / ISCP-Data : A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 3246-3250 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

@inproceedings{a64785f11de14f18bf2e399e3441b47d,

title = "ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application",

abstract = "Vehicle-to-grid (V2G) is an emerging technology for grid stability and environmental protection. However, predicting the unknown data accurately in V2G optimization is difficult. And utilizing the machine learning and big data method for V2G operation becomes a feasible method. Therefore, a vehicle-to-grid dataset for commercial center and its machine learning application are conducted in this paper. Firstly, three categories of datasets, i.e., conventional load data, PV output data and PEV charging data, are selected as the trend of power grid operation. Secondly, a V2G scenario of commercial center is designed, and the maximum values of three parameters were calculated. Next, both the centralized method and the distributed method are used for data annotation. In case study, the dataset is applied through a variety of machine learning methods. The results show that the accuracy of V2G prediction is up to 94\%.",

keywords = "V2G dataset, centralized data annotation, distributed data annotation, machine learning, vehicle-to-grid (V2G)",

author = "Yitong Shang and Hang Yu and Ziyun Shao and Linni Jian",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 ; Conference date: 22-10-2021 Through 25-10-2021",

year = "2021",

doi = "10.1109/EI252483.2021.9713203",

language = "英语",

series = "5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3246--3250",

booktitle = "5th IEEE Conference on Energy Internet and Energy System Integration",

address = "美国",

}

Shang, Y, Yu, H, Shao, Z & Jian, L 2021, ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. 在 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021, Institute of Electrical and Electronics Engineers Inc., 页码 3246-3250, 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021, Taiyuan, 中国, 22/10/21. https://doi.org/10.1109/EI252483.2021.9713203

ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. / Shang, Yitong; Yu, Hang; Shao, Ziyun 等.
5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 3246-3250 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - ISCP-Data

T2 - 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

AU - Shang, Yitong

AU - Yu, Hang

AU - Shao, Ziyun

AU - Jian, Linni

PY - 2021

Y1 - 2021

N2 - Vehicle-to-grid (V2G) is an emerging technology for grid stability and environmental protection. However, predicting the unknown data accurately in V2G optimization is difficult. And utilizing the machine learning and big data method for V2G operation becomes a feasible method. Therefore, a vehicle-to-grid dataset for commercial center and its machine learning application are conducted in this paper. Firstly, three categories of datasets, i.e., conventional load data, PV output data and PEV charging data, are selected as the trend of power grid operation. Secondly, a V2G scenario of commercial center is designed, and the maximum values of three parameters were calculated. Next, both the centralized method and the distributed method are used for data annotation. In case study, the dataset is applied through a variety of machine learning methods. The results show that the accuracy of V2G prediction is up to 94%.

AB - Vehicle-to-grid (V2G) is an emerging technology for grid stability and environmental protection. However, predicting the unknown data accurately in V2G optimization is difficult. And utilizing the machine learning and big data method for V2G operation becomes a feasible method. Therefore, a vehicle-to-grid dataset for commercial center and its machine learning application are conducted in this paper. Firstly, three categories of datasets, i.e., conventional load data, PV output data and PEV charging data, are selected as the trend of power grid operation. Secondly, a V2G scenario of commercial center is designed, and the maximum values of three parameters were calculated. Next, both the centralized method and the distributed method are used for data annotation. In case study, the dataset is applied through a variety of machine learning methods. The results show that the accuracy of V2G prediction is up to 94%.

KW - V2G dataset

KW - centralized data annotation

KW - distributed data annotation

KW - machine learning

KW - vehicle-to-grid (V2G)

UR - https://www.scopus.com/pages/publications/85128209515

U2 - 10.1109/EI252483.2021.9713203

DO - 10.1109/EI252483.2021.9713203

M3 - 会议稿件

AN - SCOPUS:85128209515

T3 - 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

SP - 3246

EP - 3250

BT - 5th IEEE Conference on Energy Internet and Energy System Integration

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 22 October 2021 through 25 October 2021

ER -

Shang Y, Yu H, Shao Z, Jian L. ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. 在 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 页码 3246-3250. (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). doi: 10.1109/EI252483.2021.9713203

ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application

摘要

丛书

会议

联合国可持续发展目标

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