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Forecasting annual natural gas consumption via the application of a novel hybrid model

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An...

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Detalles Bibliográficos
Autores principales: Gao, Feng, Shao, Xueyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790315/
https://www.ncbi.nlm.nih.gov/pubmed/33415637
http://dx.doi.org/10.1007/s11356-020-12275-w
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author Gao, Feng
Shao, Xueyan
author_facet Gao, Feng
Shao, Xueyan
author_sort Gao, Feng
collection PubMed
description Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.
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spelling pubmed-77903152021-01-08 Forecasting annual natural gas consumption via the application of a novel hybrid model Gao, Feng Shao, Xueyan Environ Sci Pollut Res Int Research Article Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025. Springer Berlin Heidelberg 2021-01-07 2021 /pmc/articles/PMC7790315/ /pubmed/33415637 http://dx.doi.org/10.1007/s11356-020-12275-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Gao, Feng
Shao, Xueyan
Forecasting annual natural gas consumption via the application of a novel hybrid model
title Forecasting annual natural gas consumption via the application of a novel hybrid model
title_full Forecasting annual natural gas consumption via the application of a novel hybrid model
title_fullStr Forecasting annual natural gas consumption via the application of a novel hybrid model
title_full_unstemmed Forecasting annual natural gas consumption via the application of a novel hybrid model
title_short Forecasting annual natural gas consumption via the application of a novel hybrid model
title_sort forecasting annual natural gas consumption via the application of a novel hybrid model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790315/
https://www.ncbi.nlm.nih.gov/pubmed/33415637
http://dx.doi.org/10.1007/s11356-020-12275-w
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