Sliding window and dual-channel CNN (SWDC-CNN): A novel method for synchronous prediction of coal and electricity consumption in cement calcination process

Abstract

As a representative of the process industry, the cement industry consumes a large amount of coal and electricity resources. This is mainly due to the irrational energy scheduling caused by the rough production and independent statistics of each energy consumption indicator within the cement industry. The synchronous accurate prediction of energy consumption can provide a more resultful scheme for the production control process and energy scheduling. However, due to the time delay, variables coupling, and uncertainty of production, it is difficult to synchronously forecast multiple indicators. In this paper, a data-driven prediction method combining Sliding Window and Dual-Channel Convolutional Neural Network (SWDC-CNN) is proposed to achieve synchronous prediction of coal and electricity consumption in the next 5 min. The sliding window method is designed to extract time-varying delay characteristics of time series data to overcome its influence on energy consumption prediction. The effect of redundant parameters between weakly correlated variables on energy prediction is reduced by designing a dual-channel structure. We experimented and compared with excellent models Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gate Recurrent Unit (GRU) on an actual cement production data in Shanxi Province, China. Experimental results show that the proposed SWDC-CNN model has good performance, the highest prediction accuracy, and can meet the expected requirements.