Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics

Baoju Li; Wenting Wang; Yong Sun; Pupu Chao; Xueguang Zhang; Weixing Li

doi:10.1109/EI252483.2021.9713113

Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics

Baoju Li
, Wenting Wang
, Yong Sun
, Pupu Chao
, Xueguang Zhang
, Weixing Li^*

^*Corresponding author for this work

School of Electrical Engineering and Automation

Harbin Institute of Technology
Ltd.

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

1 Scopus citations

Abstract

With the improvement of the consumption potential of new energy assisted by thermal system, the coupling and tightness of power and heat supply are continuously strengthened. Higher requirements are put forward for probability prediction of thermal load. In the existing research, point prediction models are usually established according to the relevant characteristics of thermal system, which is difficult to reflect the influence of the uncertainty of thermal load and the electrical-thermal coupling characteristics on the thermal load prediction result. In view of this, a probabilistic forecasting method of short-term thermal load considering electrical-thermal coupling characteristics is proposed. Firstly, the collected data is preprocessed, and the timescale is matched. Secondly, Pearson coefficient is used to analyze the electrical-thermal coupling characteristics, and the feature set considering electrical-thermal coupling is constructed. Finally, Gaussian process regression is used to establish a probabilistic forecasting model of short-term thermal load.

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	2896-2900
Number of pages	5
ISBN (Electronic)	9781665434256
DOIs	https://doi.org/10.1109/EI252483.2021.9713113
State	Published - 2021
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

Gaussian process regression
electrical-thermal coupling characteristics
new energy integration
probabilistic forecasting of short-term thermal load

Access to Document

10.1109/EI252483.2021.9713113

Cite this

Li, B., Wang, W., Sun, Y., Chao, P., Zhang, X., & Li, W. (2021). Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021 (pp. 2896-2900). (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/EI252483.2021.9713113

Li, Baoju ; Wang, Wenting ; Sun, Yong et al. / Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics. 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. 2896-2900 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

@inproceedings{9db250a9fdf24abc8932e4ba32060340,

title = "Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics",

abstract = "With the improvement of the consumption potential of new energy assisted by thermal system, the coupling and tightness of power and heat supply are continuously strengthened. Higher requirements are put forward for probability prediction of thermal load. In the existing research, point prediction models are usually established according to the relevant characteristics of thermal system, which is difficult to reflect the influence of the uncertainty of thermal load and the electrical-thermal coupling characteristics on the thermal load prediction result. In view of this, a probabilistic forecasting method of short-term thermal load considering electrical-thermal coupling characteristics is proposed. Firstly, the collected data is preprocessed, and the timescale is matched. Secondly, Pearson coefficient is used to analyze the electrical-thermal coupling characteristics, and the feature set considering electrical-thermal coupling is constructed. Finally, Gaussian process regression is used to establish a probabilistic forecasting model of short-term thermal load.",

keywords = "Gaussian process regression, electrical-thermal coupling characteristics, new energy integration, probabilistic forecasting of short-term thermal load",

author = "Baoju Li and Wenting Wang and Yong Sun and Pupu Chao and Xueguang Zhang and Weixing Li",

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/EI252483.2021.9713113",

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 = "2896--2900",

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

address = "美国",

}

Li, B, Wang, W, Sun, Y, Chao, P, Zhang, X & Li, W 2021, Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics. 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. 2896-2900, 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021, Taiyuan, China, 22/10/21. https://doi.org/10.1109/EI252483.2021.9713113

Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics. / Li, Baoju; Wang, Wenting; Sun, Yong 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. 2896-2900 (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 - Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics

AU - Li, Baoju

AU - Wang, Wenting

AU - Sun, Yong

AU - Chao, Pupu

AU - Zhang, Xueguang

AU - Li, Weixing

PY - 2021

Y1 - 2021

N2 - With the improvement of the consumption potential of new energy assisted by thermal system, the coupling and tightness of power and heat supply are continuously strengthened. Higher requirements are put forward for probability prediction of thermal load. In the existing research, point prediction models are usually established according to the relevant characteristics of thermal system, which is difficult to reflect the influence of the uncertainty of thermal load and the electrical-thermal coupling characteristics on the thermal load prediction result. In view of this, a probabilistic forecasting method of short-term thermal load considering electrical-thermal coupling characteristics is proposed. Firstly, the collected data is preprocessed, and the timescale is matched. Secondly, Pearson coefficient is used to analyze the electrical-thermal coupling characteristics, and the feature set considering electrical-thermal coupling is constructed. Finally, Gaussian process regression is used to establish a probabilistic forecasting model of short-term thermal load.

AB - With the improvement of the consumption potential of new energy assisted by thermal system, the coupling and tightness of power and heat supply are continuously strengthened. Higher requirements are put forward for probability prediction of thermal load. In the existing research, point prediction models are usually established according to the relevant characteristics of thermal system, which is difficult to reflect the influence of the uncertainty of thermal load and the electrical-thermal coupling characteristics on the thermal load prediction result. In view of this, a probabilistic forecasting method of short-term thermal load considering electrical-thermal coupling characteristics is proposed. Firstly, the collected data is preprocessed, and the timescale is matched. Secondly, Pearson coefficient is used to analyze the electrical-thermal coupling characteristics, and the feature set considering electrical-thermal coupling is constructed. Finally, Gaussian process regression is used to establish a probabilistic forecasting model of short-term thermal load.

KW - Gaussian process regression

KW - electrical-thermal coupling characteristics

KW - new energy integration

KW - probabilistic forecasting of short-term thermal load

UR - https://www.scopus.com/pages/publications/85128225709

U2 - 10.1109/EI252483.2021.9713113

DO - 10.1109/EI252483.2021.9713113

M3 - 会议稿件

AN - SCOPUS:85128225709

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

SP - 2896

EP - 2900

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

PB - Institute of Electrical and Electronics Engineers Inc.

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

Y2 - 22 October 2021 through 25 October 2021

ER -

Li B, Wang W, Sun Y, Chao P, Zhang X, Li W. Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics. 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. 2896-2900. (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). doi: 10.1109/EI252483.2021.9713113

Short-Term Thermal Load Probabilistic Forecasting Considering Electrical-Thermal Coupling Characteristics

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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