## 1 Introduction  
Natural gas, a clean and efficient energy source, plays a significant role in the global energy consumption structure. Widely utilized in power generation, heating, and industrial applications, it is a cornerstone of modern energy systems. According to the World Energy Statistics Yearbook 2023, natural gas consumption has increased significantly in North America, with notable demand growth in the industrial and power sectors of the United States and Canada. In China, despite a slight decline in 2022, total consumption still reached 358 billion cubic meters, highlighting its essential role in supporting energy demand and economic growth. Accurate forecasting of gas purchases is vital for effective gas supply chain management, enabling suppliers to plan procurement schedules effectively and maintain efficient operations to meet growing demand. In Europe, concerns over supply disruptions and record-high prices have led to a notable decrease in natural gas consumption, underscoring the importance of precise forecasting to balance supply and demand, avoid shortages, and minimize costs. High-accuracy forecasts of gas purchases not only help companies stockpile gas in anticipation of peak demand but also optimize procurement plans, minimize economic losses, and enhance customer satisfaction [1]. As natural gas continues to grow in importance within the global energy landscape, and as regional and economic challenges intensify, the need for innovative solutions to achieve accurate purchase forecasts has become more urgent than ever. In the field of natural gas demand forecasting, researchers have employed various methods to improve prediction accuracy and reliability. Econometric models use statistical techniques to establish relationships between natural gas demand and influencing factors, such as GDP or prices [2]. However, such methods require a substantial amount of data support and often struggle to handle nonlinear relationships. Time series models forecast future demand by analyzing historical data trends. Common methods include the statistical decomposition model, autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA), which perform well in short-term forecasting [3; 4]. Artificial neural networks (ANN) train nonlinear prediction models using historical data, with common approaches including two-stage systems and multilayer perceptron (MLP). These methods excel at capturing complex nonlinear relationships [5; 6]. However, they require extensive experimentation to identify a suitable network structure, and intricate training is necessary to achieve high prediction accuracy [7]. Ensemble learning models, such as methods combining ANN, ANFIS, and traditional regression models, leverage the advantages of multiple models [8]. Currently, two ensemble learning methods, boosting and bagging, are primarily employed in natural gas demand forecasting. The stacking method, another ensemble learning approach, has the capability to mine and express highly complex nonlinear relationships. However, it has not been widely applied in natural gas demand forecasting. To address the issues identified in the existing natural gas purchase prediction model, this research introduces a new stacking ensemble prediction method for natural gas purchases that integrate multiple features. The characteristics of this model are mainly divided into three aspects: (1) Feature enhancement. For regional features, natural gas usage patterns are first categorized based on geographical characteristics. Procurement scenarios for different industries--such as commercial, industrial, and residential gas consumption--are then further delineated, providing a more detailed understanding of demand. Additionally, weather and temporal features are incorporated as basic features to account for seasonal variations and medium-term demand fluctuations [9].