A hybrid deep learning framework for predicting daily natural gas consumption

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid deep learning framework for predicting daily natural gas consumption

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A hybrid deep learning framework for predicting daily natural gas consumption

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Svazek periodika

neuveden

Číslo periodika v rámci svazku

257

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

„“-„“

Kód UT WoS článku

000853698300008

EID výsledku v databázi Scopus

2-s2.0-85133929463