A hybrid deep learning framework for predicting daily natural gas consumption

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU146473" target="_blank" >RIV/00216305:26210/22:PU146473 - isvavai.cz</a>

Result on the web

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222015924" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222015924</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.energy.2022.124689" target="_blank" >10.1016/j.energy.2022.124689</a>

Result language

angličtina

Original language name

A hybrid deep learning framework for predicting daily natural gas consumption

Original language description

Conventional time-series prediction methods for natural gas consumption mainly focus on capturing the temporal feature, neglecting static and dynamic information extraction. The accurate prediction of natural gas consumption possesses of paramount significance in the normal operation of the national economy. This paper proposes a novel method that resolves the deficiency of conventional time series prediction to address this demand via designing a hybrid deep learning framework to extract comprehensive information from gas consumption. The proposed model captures static and dynamic information via encoding gas consumption as matrices and extracts long-term dependency patterns from time series consumption. Subsequently, a customised network is proposed for information fusion. Cases from several different regions in China are studied as examples, and the proposed model is compared with other advanced approaches (such as long short-term memory (LSTM), convolution neural network long short-term memory (CNN-LSTM)). The mean absolute percentage error is reduced by a range of 0.235%-10.303% compared with other models. According to the comparison results, the proposed model provides an efficient time series prediction functionality. It is also proved that, after effectively extracting comprehensive information and integrating long-term information with static and dynamic information, the accuracy and efficiency of natural gas consumption prediction are greatly promoted. A sensitivity analysis of different modules combination is conducted to emphasise the significance of each module in the hybrid framework. The results indicate that the method coupling all these modules leads to signif-icant improvement in prediction accuracy and robustness. (c) 2022 Elsevier Ltd. All rights reserved.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20704 - Energy and fuels

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Volume of the periodical

neuveden

Issue of the periodical within the volume

257

Country of publishing house

GB - UNITED KINGDOM

Number of pages

24

Pages from-to

„“-„“

UT code for WoS article

000853698300008

EID of the result in the Scopus database

2-s2.0-85133929463