An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting

Jinyuan Liu, Shouxi Wang (), Nan Wei, Yi Yang, Yihao Lv, Xu Wang and Fanhua Zeng ()
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Jinyuan Liu: College of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China
Shouxi Wang: College of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China
Nan Wei: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510000, China
Yi Yang: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Yihao Lv: College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China
Xu Wang: International Education, University of Exeter, Exeter EX4 4PY, UK
Fanhua Zeng: Faculty of Engineering & Applied Science, University of Regina, Regina, SK S4S 0A2, Canada

Energies, 2023, vol. 16, issue 3, 1-14

Abstract: Artificial intelligence models have been widely applied for natural gas consumption forecasting over the past decades, especially for short-term consumption forecasting. This paper proposes a three-layer neural network forecasting model that can extract key information from input factors and improve the weight optimization mechanism of long short-term memory (LSTM) neural network to effectively forecast short-term consumption. In the proposed model, a convolutional neural network (CNN) layer is adopted to extract the features among various factors affecting natural gas consumption and improve computing efficiency. The LSTM layer is able to learn and save the long-distance state through the gating mechanism and overcomes the defects of gradient disappearance and explosion in the recurrent neural network. To solve the problem of encoding input sequences as fixed-length vectors, the layer of attention (ATT) is used to optimize the assignment of weights and highlight the key sequences. Apart from the comparisons with other popular forecasting models, the performance and robustness of the proposed model are validated on datasets with different fluctuations and complexities. Compared with traditional two-layer models (CNN-LSTM and LSTM-ATT), the mean absolute range normalized errors (MARNE) of the proposed model in Athens and Spata are improved by more than 16% and 11%, respectively. In comparison with single LSTM, back propagation neural network, support vector regression, and multiple linear regression methods, the improvement in MARNE exceeds 42% in Athens. The coefficient of determination is improved by more than 25%, even in the high-complexity dataset, Spata.

Keywords: natural gas consumption forecasting; conventional neural network; long short-term memory; attention mechanism; sample entropy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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