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| 基于PSO-BP模型的煤炭价格预测研究 |
| Research on coal Price prediction based on PSO-BP model |
| 投稿时间：2024-10-12 修订日期：2025-04-14 |
| DOI： |
| 中文关键词: BP神经网络、PSO-BP神经网络、煤炭价格、价格预测 |
| 英文关键词: BPNeural Networks PSO-BP Neural Networks Coal Prices Price Prediction |
| 基金项目:基于多目标决策的复杂产品研制项目风险评估研究，项目编号KJ2021A0621 |
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| 中文摘要: |
| 煤炭作为我国的一种重要的能源资源，是许多能源密集型工业，如发电、炼钢和化学品的主要燃料。它的价格波动会显著影响企业的投资决策和就业市场，有可能推动能源结构的调整，并对国际贸易产生影响。因此，准确预测煤炭价格对我国经济的发展具有重要作用。本研究以秦皇岛港口煤炭价格为预测对象，分别运用PSO-BP神经网络模型和BP神经网络模型展开预测分析。研究过程中，利用典型相关性分析筛选主要影响因素，通过实证对比分析发现，PSO-BP神经网络模型在预测煤炭价格方面表现卓越。与传统BP神经网络模型预测结果进行对比，其预测精度显著提升。因此采用PSO-BP神经网络模型对未来煤炭价格进行预测，具备极高的实践价值和理论意义。 |
| 英文摘要: |
| As China's primary energy source, coal serves as the dominant fuel for energy-intensive sectors including power generation, steel production, and chemical manufacturing. Its price volatility significantly influences corporate investment decisions and labor markets, while also driving energy structure adjustments and impacting international trade. Accurate coal price forecasting thus holds strategic importance for China's economic planning.This study focuses on Qinhuangdao Port's coal prices, employing both PSO-BP and BP neural network models for predictive analysis. Canonical correlation analysis identified key influencing factors, while empirical comparisons revealed the PSO-BP model's superior predictive accuracy over traditional BP networks. The enhanced algorithm demonstrates particular effectiveness in capturing coal market dynamics, suggesting substantial application potential for energy market forecasting. The findings highlight the PSO-BP model's theoretical and practical value for forecasting coal price trends. |
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