A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction-Reference-Cited by-同舟云学术

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Published:2022-03-10 Issue:7 Volume:10 Page:1998-2021
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Science & Engineering

Author:

Zeng Huibin¹^ORCID,Shao Bilin¹^ORCID,Bian Genqing²^ORCID,Dai Hongbin¹^ORCID,Zhou Fangyu³

Affiliation:

1. School of Management Xi'an University of Architecture and Technology Xi'an China

2. School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China

3. School of Applied English Chengdu Institute Sichuan International Studies University Chengdu China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1122

Reference44 articles.

1. A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

2. BP Energy Outlook2020.https://www.bp.com/content/dam/bp/country‐sites/zh_cn/china/home/reports/bp‐energy‐outlook/2020/energy‐outlook‐2020‐press‐release‐cn.pdf

3. International Energy Agency.World Energy Outlook2020. Accessed April 6 2021.https://www.iea.org/reports/world‐energy‐outlook‐2020

4. A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

5. Forecasting volatility of wind power production

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