Modeling and forecasting of coal price based on influencing factors and time series

Abstract

The fluctuations of coal prices substantially impact carbon pricing, the flexibility in power generation at coal-fired plants, and the pricing strategies of upstream and downstream industries. Given limitations in the existing coal price forecasting research, this study introduces grey relational analysis to determine the relational grade between influencing factors and coal prices. Results indicate that crude oil production, CPI (Consumer Price Index), coal sales of key coal mines, and PMI (Purchasing Managers Index) exhibit the highest correlation with coal prices, with grey relational coefficients all exceeding 0.9. This study also applies principal component analysis to achieve data dimensionality reduction, resulting in a 2% decrease in average forecasting error. The influence-based forecasting model developed in this study demonstrates an average forecasting accuracy exceeding 90%. To render coal price forecast more practical, this study constructs several models based on time series to forecast the coal price for the subsequent 30 days. The results show that the BP (Back Propagation) neural network model demonstrates higher forecasting accuracy than the LSTM (Long Short-Term Memory) model and ARIMA (Autoregressive Integrated Moving Average) model for 30-day coal price forecasting. The forecast generated by BP model closely match the actual coal price trends, achieving a high average prediction accuracy exceeding 85%. The accurate forecasting model of coal price proposed in this study holds significant importance in characterizing carbon price fluctuations, enhancing power plant flexibility in electricity generation, and guiding coal procurement.