2021
DOI: 10.1007/s11356-020-12275-w
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Forecasting annual natural gas consumption via the application of a novel hybrid model

Abstract: 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 ste… Show more

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Cited by 25 publications
(8 citation statements)
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“…The smaller the metric value means the higher the forecast accuracy. In the literature, MSE, root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as forecasting performance evaluation metrics (KPIs) [ [82] , [83] , [84] , [85] ]. Different KPIs are used in this study to evaluate the accuracy of the proposed methodology from various perspectives [ 86 ].…”
Section: Proposed Methodologymentioning
confidence: 99%
“…The smaller the metric value means the higher the forecast accuracy. In the literature, MSE, root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as forecasting performance evaluation metrics (KPIs) [ [82] , [83] , [84] , [85] ]. Different KPIs are used in this study to evaluate the accuracy of the proposed methodology from various perspectives [ 86 ].…”
Section: Proposed Methodologymentioning
confidence: 99%
“…Based on variable mode decomposition, particle swarm optimization, and deep belief networks, Li et al suggested a hybrid forecasting model of monthly Henry Hub natural gas prices [20]. Gao et al proposed a new hybrid forecasting model based on a support vector machine and an improved artificial fish swarm algorithm to predict annual natural gas consumption [21]. Atici and Pala used the hybrid model in their study for the Ionospheric foF2 parameter estimation [22].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Analysis of the literature indicated that the three main influencing factors with the highest correlation with natural gas demand in the structure of energy consumption, GDP and urbanization rate, successively [28]. The published results for Indonesia as a country rich in its natural resources are to be one of the countries in the world that plays an active role in increasing international trade flows, it has been shown that in the short term, all variables such as domestic consumption, exchange rate, natural gas prices, and GDP per capita have a significant impact on the volume of natural gas exports and imports [29]. Additionally, other studies indicate factors such as total population, gross domestic products, urbanization rate, industrial structure, energy consumption structure, and carbon dioxide emission to be colinear with natural gas consumption.…”
Section: Principal Characteristics Of the Variablesmentioning
confidence: 99%