Research on the monthly fluctuation of natural gas demand in China

• Published in: Energy science & engineering Vol. 10; no. 9; pp. 3602 - 3616
• Main Authors: Du, Xueping, Qiu, Shuo, Wu, Jiangtao, Meng, Xianyang
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.09.2022; Wiley
• Subjects: Algorithms; Big Data; Coal; Data analysis; Demand; Energy consumption; gas storage peak shaving; GDP; Gross Domestic Product; influencing factor; Liquefied natural gas; load forecasting; Natural gas; Natural gas supply; Neural networks; Seasonal variations; Security; Statistical analysis; Statistical methods; Summer; Time series; Wavelet transforms; Winter; United States > US; China; Europe
• ISSN: 2050-0505; 2050-0505
• DOI: 10.1002/ese3.1244

With the rapid growth of natural gas consumption in China, the monthly peak shaving and security issues of the gas supply have become increasingly prominent. In view of this situation, the monthly fluctuation law of natural gas consumption in China must be studied to guide gas storage peak shaving and gas supply planning. In this study, the concepts of the gas year (statistical method of breaking through the calendar year to gas year and reflecting the complete gas consumption cycle), typical year (the year that had the representative load curve of China), and some fluctuation characteristics parameters were applied to study the monthly fluctuation law of natural gas consumption in China. Furthermore, according to the monthly statistical data of China, including natural gas demand, power generation, crude steel output, refined copper output, the monthly average temperature in the typical city, the relationship between influencing factors and natural gas demand was analyzed by the gray relative correlation method. Based on the stepwise regression Cobb–Douglas (C–D) production function, the natural gas demand of China in the gas year of 2030/31 was predicted. The research results can be used for energy planning, statistics, peak shaving of gas storage, liquefied natural gas trade, and can also be used as a reference for energy big data analysis and refined management. According to the monthly statistical data of China, including natural gas demand, power generation, the monthly average temperature in the typical city, the relationship between influencing factors and natural gas demand was analyzed by the gray relative correlation method. Based on the stepwise regression Cobb–Douglas (C–D) production model, the natural gas demand of China in the gas year of 2030/31 was predicted.