## 5 Conclusions  
The accurate prediction of natural gas purchase volumes is critical for optimizing resource allocation, improving decision-making processes, and ensuring efficient supply chain management. This study introduces a novel approach by integrating fine-grained features, such as regional and usage attributes, into a stacking ensemble framework to address the challenges of modeling complex nonlinear relationships. The proposed method combines three backbone models--Informer, MLR, and SVR--within a stacking ensemble framework, consisting of 12 base learners, to effectively leverage weather, regional, and usage features. Experimental results demonstrate that incorporating these features significantly improves prediction accuracy. The stacking ensemble model consistently outperformed the individual base models across all evaluation metrics, including \(R^{2}\), MAE, MRE, SMAPE, and MAPE. For example, the \(R^{2}\) values of the Informer, MLR, and SVR showed notable improvements after integrating weather, regional, and usage features, with \(R^{2}\) values improving by 4-15% compared to the 12 base models. Additionally, the stacking ensemble model maintained a reasonable prediction error, with the maximum error validated at 5.39% from November 2023 to October 2024, even under potential economic downturns. These results highlight its robust generalization capabilities and practical applicability, demonstrating the effectiveness of integrating fine-grained features and ensemble learning techniques for accurate natural gas purchase volume prediction. Although the stacking ensemble method demonstrated excellent predictive performance, several limitations must be acknowledged. First, due to the inclusion of 12 base models, the model exhibits relatively high computational complexity, which may restrict its applicability to larger datasets or real-time forecasting scenarios. Future research could leverage distributed training or parallel computing techniques to improve the model's computational efficiency and scalability. Second, since the data used in this study were collected exclusively from Pi County, Chengdu, the model's applicability to other geographical regions or economic conditions has yet to be validated. Expanding the dataset and testing the model in diverse contexts will be essential to confirm its generalizability. In addition, while this study successfully mitigated the risk of overfitting through techniques such as cross-validation and L2 regularization, further optimization of the model architecture is necessary to strike a balance between complexity and performance, preventing potential overfitting on other datasets. Future research will also explore more advanced \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Stacking Predicted Value** & **Actual Value** & **Percentage Error (\%)** \\ \hline
2023.11 & 11,259,021 & 10,683,648 & 5.39 \\
2023.12 & 15,951,523 & 15,226,730 & 4.76 \\
2024.01 & 16,701,125 & 17,109,964 & \(-\)2.39 \\
2024.02 & 14,225,391 & 13,843,689 & 2.76 \\
2024.03 & 13,317,303 & 13,270,379 & 0.35 \\
2024.04 & 10,375,301 & 9,896,262 & 4.84 \\
2024.05 & 10,199,972 & 9,680,592 & 5.37 \\
2024.06 & 9,148,206 & 8,778,636 & 4.21 \\
2024.07 & 8,987,714 & 8,687,648 & 3.45 \\
2024.08 & 8,189,393 & 7,973,548 & 2.71 \\
2024.09 & 8,836,944 & 9,127,189 & \(-\)3.18 \\
2024.10 & 10,923,210 & 10,400,086 & 5.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of future predictions and actual values. ensemble learning techniques (e.g., XGBoost and LightGBM) and conduct comparative analyses with other nonlinear algorithms to comprehensively evaluate their performance in modeling complex nonlinear relationships. Lastly, plans are in place to incorporate additional external influencing factors, such as economic indicators and population changes, to further enhance predictive accuracy and provide robust, efficient decision-making support for natural gas procurement. Investigation, Y.L., Q.Y. and Y.B. ; methodology and software, J.W. and L.J.