A hybrid deep learning framework for predicting daily natural gas consumption.

Conventional time-series prediction methods for natural gas consumption mainly focus on capturing the temporal feature, neglecting static and dynamic information extraction. The accurate prediction of natural gas consumption possesses of paramount significance in the normal operation of the national economy. This paper proposes a novel method that resolves the deficiency of conventional time series prediction to address this demand via designing a hybrid deep learning framework to extract comprehensive information from gas consumption. The proposed model captures static and dynamic information via encoding gas consumption as matrices and extracts long-term dependency patterns from time series consumption. Subsequently, a customised network is proposed for information fusion. Cases from several different regions in China are studied as examples, and the proposed model is compared with other advanced approaches (such as long short-term memory (LSTM), convolution neural network long short-term memory (CNN-LSTM)). The mean absolute percentage error is reduced by a range of 0.235%–10.303% compared with other models. According to the comparison results, the proposed model provides an efficient time series prediction functionality. It is also proved that, after effectively extracting comprehensive information and integrating long-term information with static and dynamic information, the accuracy and efficiency of natural gas consumption prediction are greatly promoted. A sensitivity analysis of different modules combination is conducted to emphasise the significance of each module in the hybrid framework. The results indicate that the method coupling all these modules leads to significant improvement in prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]