Abstract
Natural gas consumption has increased with an average annual growth rate of about 10% between 2012 and 2017. Total natural gas consumption accounted for 6.4% of consumed primary energy resources in 2016, up from 5.4% in 2012, making China the world’s third-largest gas user. Therefore, accurately predicting natural gas consumption has become very important for market participants to organize indigenous production, foreign supply contracts and infrastructures in a better way. This paper first presents the main factors affecting China’s natural gas consumption, and then proposes a hybrid forecasting model by combining the particle swarm optimization algorithm and wavelet neural network (PSO-WNN). In PSO-WNN model, the initial weights and wavelet parameters are optimized using PSO algorithm and updated through a dynamic learning rate to improve the training speed, forecasting precision and reduce fluctuation of WNN. The experimental results show the superiority of the proposed model compared with ANN and WNN based models. Then, this study conducts the scenario analysis of the natural gas consumption from 2017 to 2025 in China based on three scenarios, namely low scenario, reference scenario and high scenario, and the results illustrate that the China’s natural gas consumption is going to be 342.70, 358.27, 366.42 million tce (“standard” tons coal equivalent) in 2020, and 407.01, 437.95, 461.38 million tce in 2025 under the low, reference and high scenarios, respectively. Finally, this paper provides some policy suggestions on natural gas exploration and development, infrastructure construction and technical innovations to promote a sustainable development of China’s natural gas industry.
Highlights
With fast growing urbanization and economic development, worldwide energy consumption has increased by 30% in the last 25 years [1]
In China, natural gas consumption has increased with an average annual growth rate of about 10% between
Wang et al proposed a novel hybrid electricity price forecasting model by combining a two-layer decomposition technique and back propagation (BP) neural network, and the results show the efficiency of the proposed model [7]
Summary
With fast growing urbanization and economic development, worldwide energy consumption has increased by 30% in the last 25 years [1]. Niu and Dai established a new short-term load predicting method by combining the empirical mode decomposition, gray relational analysis, and modified particle swarm optimization with the LSSVM model. The former two modules were applied to process the original load series, and the latter was used to predict the preprocessed subsequences [11]. The main novelties and contributions of this study can be denoted in the following two respects: (1) This study establishes a novel ANN-based hybrid model by combining the PSO algorithm, wavelet analysis and a modified learning rate scheme for the scenario analysis of natural gas consumption in China.
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