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

Author:

Liu Jinyuan1,Wang Shouxi1,Wei Nan2,Yang Yi3,Lv Yihao3,Wang Xu4,Zeng Fanhua5

Affiliation:

1. College of Petroleum Engineering, Southwest Petroleum University, Chengdu 610500, China

2. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510000, China

3. College of Petroleum Engineering, Xi’an Shiyou University, Xi’an 710065, China

4. International Education, University of Exeter, Exeter EX4 4PY, UK

5. Faculty of Engineering & Applied Science, University of Regina, Regina, SK S4S 0A2, Canada

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.

Funder

China Scholarship Council

Guangdong Basic and Applied Basic Research Foundation

Guangzhou Basic Research Program-City School (College) Joint Funding Project

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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