Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network

Accurate gas consumption and elapsed time forecasting can help decision-makers detect anomaly gas usage and notify users to recognize the facility fault in real-time. However, it is challenging due to its variable and complex factors. This paper proposed a novel deep model, named multi-channel DNN (...

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Autores principales:	Yeonjee Choi, Xiaorui Shao, Hyun Suk Hwang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Smart factory gas consumption forecasting deep learning multivariate time series forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/2245f4490d44432493268d92ba287031
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spelling	oai:doaj.org-article:2245f4490d44432493268d92ba2870312021-12-03T00:00:25ZHighly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network2169-353610.1109/ACCESS.2021.3129601https://doaj.org/article/2245f4490d44432493268d92ba2870312021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9622209/https://doaj.org/toc/2169-3536Accurate gas consumption and elapsed time forecasting can help decision-makers detect anomaly gas usage and notify users to recognize the facility fault in real-time. However, it is challenging due to its variable and complex factors. This paper proposed a novel deep model, named multi-channel DNN (MC-DNN), for gas consumption and elapsed time forecasting by applying three time-series channels, including time variable, temperature, and historical gas consumption. Significantly, the time variable channel records time information to reflect the heating process pattern; the environmental channel records the inside temperature of the furnace to reflect the environmental factors and the raw consumption channel provides the historical gas consumption trend. A fully connected deep neural network (DNN) is adopted to extract the rich hidden patterns from three parallel channels in the proposed deep model. The extracted hidden features are combined as the fusion features for gas consumption and elapsed time forecasting. Besides, two-loss functions are utilized to connect two tasks, which increase the forecasting accuracy again. The authors verified the proposed method on the real-time dataset of the forging factory using multiple evaluation metrics. Massive experiments have proved that it can accurately forecast gas consumption and elapsed time at the same time.Yeonjee ChoiXiaorui ShaoHyun Suk HwangIEEEarticleSmart factorygas consumption forecastingdeep learningmultivariate time series forecastingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157447-157457 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Smart factory gas consumption forecasting deep learning multivariate time series forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Smart factory gas consumption forecasting deep learning multivariate time series forecasting Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yeonjee Choi Xiaorui Shao Hyun Suk Hwang Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
description	Accurate gas consumption and elapsed time forecasting can help decision-makers detect anomaly gas usage and notify users to recognize the facility fault in real-time. However, it is challenging due to its variable and complex factors. This paper proposed a novel deep model, named multi-channel DNN (MC-DNN), for gas consumption and elapsed time forecasting by applying three time-series channels, including time variable, temperature, and historical gas consumption. Significantly, the time variable channel records time information to reflect the heating process pattern; the environmental channel records the inside temperature of the furnace to reflect the environmental factors and the raw consumption channel provides the historical gas consumption trend. A fully connected deep neural network (DNN) is adopted to extract the rich hidden patterns from three parallel channels in the proposed deep model. The extracted hidden features are combined as the fusion features for gas consumption and elapsed time forecasting. Besides, two-loss functions are utilized to connect two tasks, which increase the forecasting accuracy again. The authors verified the proposed method on the real-time dataset of the forging factory using multiple evaluation metrics. Massive experiments have proved that it can accurately forecast gas consumption and elapsed time at the same time.
format	article
author	Yeonjee Choi Xiaorui Shao Hyun Suk Hwang
author_facet	Yeonjee Choi Xiaorui Shao Hyun Suk Hwang
author_sort	Yeonjee Choi
title	Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
title_short	Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
title_full	Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
title_fullStr	Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
title_full_unstemmed	Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network
title_sort	highly accurate short-term gas consumption and elapsed time forecasting using multi-channel deep neural network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/2245f4490d44432493268d92ba287031
work_keys_str_mv	AT yeonjeechoi highlyaccurateshorttermgasconsumptionandelapsedtimeforecastingusingmultichanneldeepneuralnetwork AT xiaoruishao highlyaccurateshorttermgasconsumptionandelapsedtimeforecastingusingmultichanneldeepneuralnetwork AT hyunsukhwang highlyaccurateshorttermgasconsumptionandelapsedtimeforecastingusingmultichanneldeepneuralnetwork
_version_	1718373977347325952

Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network

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