A combined approach for forecasting regional long-term natural gas demand integrating <i>K</i>-means clustering, grey theory, and BP neural network

Abstract

[Objective] Factors impacting the demand of natural gas vary across regions in China, resulting in distinct data characteristics within corresponding datasets. The availability of adequate sample data for long-term forecasting is limited. Therefore, the development of a generalized demand forecasting model applicable to diverse regions remains a significant challenge. [Methods] This study focused on analyzing eleven prefecture-level cities in Shandong Province. Data from these regions, spanning multiple time periods, were gathered to establish a general database, incorporating key factors influencing natural gas demand, such as annual consumption, GDP, and population. Pearson’s correlation coefficient was initially employed to preliminarily screen and identify sample characteristics, followed by the utilization of the K-means clustering algorithm for data clustering. Subsequently, three sample points exhibiting a similar energy consumption structure were selected, and their corresponding natural gas demands at the next time points were used as new sample characteristics. Furthermore, forecast outputs derived from the grey theory were utilized as input samples for a Back Propagation (BP) neural network. As a result, a combined forecasting model was developed by integrating the new sample data with the BP neural network.[Results] The proposed forecasting method, which integrated K-means clustering, the grey theory, and a BP neural network, leveraged the historical natural gas demands of the cities with similar energy structures. By taking advantage of the grey theory’s high robustness in dealing with small sized sample data, the proposed forecasting method kept the mean absolute percentage errors of the resulting long-term natural gas demand forecasts for the 11 prefecture-level cities in Shandong Province within the range from 0.57% to 6.41%. Comparative analysis with traditional grey forecasting models, BP neural network models, and K-means clustering + BP neural network models revealed the superiority of the proposed approach, yielding improved prediction results with smaller errors and higher stability. [Conclusion] The proposed forecasting method proves effective in providing technical support for analyzing future natural gas demands in cities across China, surpassing regional limitations. Moreover, it serves as a valuable tool for assisting governments and enterprises at all levels in making decisions on allocation plans of natural gas resources.

Keywords

• natural gas
• <i>k</i>-means clustering
• bp neural network
• grey theory
• region
• demand forecast