Prediction of gas consumption based on LSTM-BPNN hybrid model

ABSTRACT

The economic benefits of iron and steel enterprises require the support of Byproduct gas prediction. A novel hybrid model for data pre-processing is proposed to ensure the stability and accuracy of gas prediction. First, each component obtained from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposition is processed using Sample Entropy (SE), and the members are denoised and restructured by singular spectrum analysis (SSA). Then, the restructured data are predicted by the Back Propagation Neural Network (BPNN) and Long Short-Term Memory (LSTM) and weighted by Particle Swarm Optimization (PSO). Finally, the data are integrated to obtain the final results. The novel hybrid model makes allowances for the linear and nonlinear characteristics of the series and successfully overcomes the limitations of individual models to get accurate and stable prediction results. The results show that the prediction accuracy is improved by 20% on average by adopting the novel hybrid model.

KEYWORDS:

• Gas prediction
• data preprocessing
• hybrid model
• regression model
• neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Natural Science Foundation of Hebei Province [F2018209201]