Research on forecasting of daily gas consumption of city gate stations of natural gas pipeline network based on deep learning

Lin Gao; Binfei Zhu; Haipeng Min; Ke Zhang

doi:10.1117/12.3039332

4 September 2024 Research on forecasting of daily gas consumption of city gate stations of natural gas pipeline network based on deep learning

Lin Gao, Binfei Zhu, Haipeng Min, Ke Zhang

Author Affiliations +

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592X (2024) https://doi.org/10.1117/12.3039332
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

Accurate forecasting of daily gas consumption of each station in a natural gas pipeline network is of great importance for natural gas companies to plan pipeline transportation capacity, formulate business plans, and carry out production scheduling and operation management. Daily natural gas consumption of gate stations is a complex time series data affected by factors such as season, cycle, and production plan. Challenges in forecasting daily gas consumption include the complexity of time-series data, the impact of external factors, and the need for effective integration of multiple features. Traditional single neural network models, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), struggle to adequately address these challenges. In this paper, a prediction model based on deep learning theory is proposed. The model is a combination of Long Short-Term Memory Network (LSTM) and Parallel Convolutional Neural Network (PCNN). The model uses PCNN to automatically extract the time series data features of natural gas gate station gas consumption and capture its changing trend; LSTM is used to capture the time series dependence of gas consumption. The proposed model aims to overcome the limitations of traditional models by integrating LSTM and PCNN to enhance feature extraction and capture complex dependencies in the data. The experimental results show that the proposed method has high prediction accuracy and stability, and can adapt to the complex and changeable pipeline network transportation environment, which provides strong support for natural gas pipeline network transportation management and optimization.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lin Gao, Binfei Zhu, Haipeng Min, and Ke Zhang "Research on forecasting of daily gas consumption of city gate stations of natural gas pipeline network based on deep learning", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592X (4 September 2024); https://doi.org/10.1117/12.3039332

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KEYWORDS

Data modeling

Education and training

Performance modeling

Deep learning

Feature extraction

Convolutional neural networks

Neural networks

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