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 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.

Keywords: Improved artificial fish swarm algorithm; Natural gas consumption forecasting; Support vector machine.

Fig. 1

Changing trend of μ with iteration increasing

Fig. 2

Flow chart of IAFSA

Fig. 3

Framework of NGC forecasting based on IAFSA-SVM

Fig. 4

Forecasting results of different models

Fig. 5

Boxplot of relative error for different models