Raw coal production prediction model based on CNN-LSTM
To accurately predict raw coal production,this paper chooses to combine convolutional neural network(CNN)and long short-term memory(LSTM)algorithms to establish a raw coal production prediction model based on CNN-LSTM.Monthly data on China's raw coal production from January 2010 to December 2021 are used as the training set,and data from January 2022 to December 2022 are used as the test set.The raw coal production from January 2023 to December 2023 was predicted using the trained model.The model predictions are evaluated by comparing them with two other single models and based on the absolute relative error.The results show that the absolute maximum error between the raw coal production prediction results of the CNN-LSTM model and the actual value is 4.98%,the prediction accuracy is significantly improved,and the monthly raw coal production prediction results for the whole year of 2023 are obtained,which provides a scientific guiding basis for the future development of the country and enterprise planning.
safety engineeringforecast of raw coal productionCNN-LSTMabsolute relative errorprediction accuracy
NETL
NSTL
万方数据
