Henan Institute of Territorial Space Survey and Planning

未来相当长的一段时间内,煤炭仍将在河南省现代能源体系中占据重要地位。针对河南省这样的煤炭生产消费大省开展煤炭消费需求预测研究,有利于省级层面管理地质勘查项目、制定开发利用政策、调整能源结构。结合格兰杰因果性检验、灰色关联分析模型,研究在ARIMA模型基础上,选取工业增加值、能源消费总量等2个指标与ARIMA模型预测残差作为LSTM模型输入,构建了ARIMA-LSTM模型。结果表明,将ARIMA模型的残差和其他相关因素作为LSTM模型的输入构成的ARIMA-LSTM模型相对于单一ARIMA模型性能显著提升。2023年河南省煤炭能源消费量预测结果为15 130.36万tce,与2022年相比,出现下行趋势,但仍然处在较高水平。研究受公开数据限制,仅进行模型适用性探究,建议相关部门在调整能源结构的同时,开展时效性更强的煤炭消费预测工作,提前做好煤炭储备,避免缺煤、限电等能源供应问题。

Coal is expected to maintain a significant role in Henan Province's modern energy system for the foreseeable future. Conducting a coal consumption demand forecasting study for a large coal producing and consuming province like Henan Province is conducive to the management of geological survey projects, formulation of development and utilisation policies, and adjustment of energy structure at the provincial level. Combined with Granger causality test and grey correlation analysis model, this study constructed an ARIMA-LSTM model based on the ARIMA model by selecting 2 indicators such as value added of industry and total energy consumption with the residuals of the prediction of the ARIMA model as inputs to the LSTM model. Results demonstrate a notable performance improvement with the ARIMA-LSTM model compared to the standalone ARIMA model. The forecasted coal energy consumption for Henan Province in 2023 is approximately 15,130.36 ten thousand tons of standard coal. This indicates a decrease from 2022 but remains high. Due to limitations in publicly available data, this study only focuses on assessing model applicability. It suggests that relevant departments should continue increasing reserves and production. To address this, it is recommended to adjust the energy structure, conduct more timely coal consumption forecasting, and prepare coal reserves early to avoid coal shortages and power restrictions.

宋昆鹏, 宋亚开. 基于ARIMA-LSTM模型的煤炭能源消费预测研究——以河南省为例. 煤炭经济研究. 2024, 44(9): 48-54

SONG Kunpeng, SONG Yakai. Coal energy consumption forecasting research based on ARIMA-LSTM model: A case study of Henan Province. Coal Economic Research. 2024, 44(9): 48-54