基于CEEMD-ELM-ARIMA的天然气价格预测模型研究
Research on natural gas price forecasting model based on CEEMD-ELM-ARIMA
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- 引用格式:
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张金良,刘子毅,王明雪.基于CEEMD-ELM-ARIMA的天然气价格预测模型研究[J].天然气与石油,2021,39(4):129-136.doi:10.3969/j.issn.1006-5539.2021.04.022
ZHANG Jinliang, LIU Ziyi, WANG Mingxue.Research on natural gas price forecasting model based on CEEMD-ELM-ARIMA [J].Natural Gas and Oil,2021,39(4):129-136.doi:10.3969/j.issn.1006-5539.2021.04.022
- DOI:
- 10.3969/j.issn.1006-5539.2021.04.022
- 作者:
- 张金良 刘子毅 王明雪
ZHANG Jinliang, LIU Ziyi, WANG Mingxue
- 作者单位:
- 华北电力大学经济与管理学院, 北京 102206
School of Economics and Management, North China Electric Power University, Beijing, 102206, China
- 关键词:
- 天然气价格预测;极限学习机;互补集成经验模态分解;混合模型;信号处理
Natural gas price prediction; Extreme Learning Machine; Complementary Ensemble Empirical Mode Decomposition; Hybrid model; Signal processing
- 摘要:
- 天然气作为清洁能源,近年成为各国常用能源之一。天然气价格是天然气企业进行重大经营决策以及运营计划的重要影响因素,准确预测天然气价格对天然气产业具有重大意义。首先采用互补集成经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)对天然气价格进行分解；其次利用极限学习机(Extreme Learning Machine,ELM)预测高频分量,差分整合移动自回归平均模型(Autoregressive Integrated Moving Average model,ARIMA)预测低频分量;最后各分量预测结果总和即为最终预测结果。用美国Henry Hub交易中心公布的数据进行预测,并与其他模型进行比较，算例结果表明CEEMD-ELM-ARIMA预测模型的预测精度更高。
Natural gas, as a clean energy source, has started to become one of the common energy sources used by many countries in recent years. Natural gas price is an important factor influencing the major business decisions and operational plans made by natural gas companies. The ability to accurately predict the price of natural gas is of great significance to the natural gas industry. Firstly, the natural gas price is broken down using Complementary Ensemble Empirical Mode Decomposition (CEEMD). Secondly, the Extreme Learning Machine (ELM) is used to predict the high-frequency component, and the differential integration of Autoregressive Integrated Moving Average Model (ARIMA) is adopted to predict the low-frequency component. The final prediction result is the sum of the prediction results of each component. The data published by USA Henry Hub Trading Center is used for prediction, and the results of the approach proposed in this paper are compared with other models. The results show that the forecasting model proposed in this paper has higher prediction accuracy.
