Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model

Xiaoyu Wang,Dongkun Luo,Wenhuan Wang,Guixin Jie,Jianye Liu

doi:10.1155/2017/6045708

Abstract

The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO) algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1)) and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China’s natural gas consumption will grow rapidly over the next five years and reach 354.1 billion cubic meters (bcm) by 2020. Moreover, the spatial distribution of natural gas consumption will shift from being supply oriented towards being demand driven and will be mainly concentrated in coastal and developed provinces.

Highlights

As a clean low-carbon energy, natural gas is a realistic choice for the sustainable development of China’s energy supply
From the individual point of view, the mean absolute percent error (MAPE) of 30 areas for the hybrid multiverse optimizer (MVO)-Nash NGBM (NNGBM) (1,1) model is less than 10%, followed by the grey Verhulst model (28 areas), the nonlinear grey Bernoulli model (NGBM) (1,1) model (27 areas), and the traditional GM (1,1) model (16 areas)
The results show that the hybrid MVO-NNGBM (1,1) model exhibits much better prediction performance than all other models

Summary

Introduction

As a clean low-carbon energy, natural gas is a realistic choice for the sustainable development of China’s energy supply. Accelerating the development of the natural gas industry and increasing the proportion of natural gas in primary energy consumption are an effective method for resolving environmental constraints, improving air quality, and achieving low-carbon sustainable development in China. China’s natural gas consumption has rapidly increased, and the contradiction between domestic supply and demand has been prominent [1]. Because China’s natural gas consumption has significant regional differences [2], pipeline construction should be prioritized in areas with rapidly increasing gas demands. Various methods and technologies have been used to predict natural gas consumption. Huntington [4] investigated industrial natural gas consumption via the autoregressive distributed lag (ADL) method. Taspinar et al [9] used a seasonal ARIMAX model to predict daily natural gas consumption

Objectives

Results

Conclusion