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^*

^*Corresponding author for this work

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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%.

Original language	English
Title of host publication	5th IEEE Conference on Energy Internet and Energy System Integration
Subtitle of host publication	Energy Internet for Carbon Neutrality, EI2 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3246-3250
Number of pages	5
ISBN (Electronic)	9781665434256
DOIs	https://doi.org/10.1109/EI252483.2021.9713203
State	Published - 2021
Externally published	Yes
Event	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 - Taiyuan, China Duration: 22 Oct 2021 → 25 Oct 2021

Publication series

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

Conference

Conference	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
Country/Territory	China
City	Taiyuan
Period	22/10/21 → 25/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

V2G dataset
centralized data annotation
distributed data annotation
machine learning
vehicle-to-grid (V2G)

Access to Document

10.1109/EI252483.2021.9713203

Cite this

Shang, Y., Yu, H., Shao, Z., & Jian, L. (2021). ISCP-Data: A Vehicle-to-grid Dataset For Commercial Center And Its Machine Learning Application. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021 (pp. 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 et al. / 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. pp. 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. in 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., pp. 3246-3250, 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021, Taiyuan, China, 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 et al.
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. p. 3246-3250 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In 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. p. 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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