Urban Gas Load Forecasting Based on Time Series Methods

Wang, Pingsheng; Zhao, Ruibao; Guo, Yongxing; Wu, Junjie; Hou, Fenglin; Zhu, Yuan-Xing

doi:10.1007/s10553-022-01458-5

Chem Technol Fuels Oils

2022

DOI: 10.1007/s10553-022-01458-5

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Urban Gas Load Forecasting Based on Time Series Methods

Pingsheng Wang

Ruibao Zhao²,

Yongxing Guo³

et al.

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Cited by 2 publications

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“…The core of model-driven method lies in constructing mathematical models to describe gas load change patterns [4]. Reference [5] fully explored the temperature effect on natural gas load demand and developed a Prophet-based regression analysis mechanism to provide a solution for optimal natural gas load management. Reference [6] is endowed with the superiority in natural gas prediction ability by combining the merits of autoregressive model and moving average strategy.…”

mentioning

confidence: 99%

A multivariate natural gas load forecasting method based on residual recurrent neural network

Ni,

Yang,

Qin

et al. 2023

Electronics Letters

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Current natural gas load forecasting encounters with the conundrum of unsatisfying accuracy and interpretability. To address the challenge, a multi‐variate forecasting method is proposed, which contains three phases: First, an integrate history‐climate‐holiday factor set is established to provide multi‐perspective for a more explainable forecast; Second, factor fusion interaction between features and instances is carried out based on hierarchical contrastive learning, which contributes to inter‐intra factors potential relationships exploration. Third, a multivariate forecasting model named ResRNN is trained using fused target dataset. Due to its innovation in structure and loss, forecasting accuracy is further improved. Finally, the authors’ method's superiority is confirmed by several groups of comparative experiments and results demonstrate that it outperforms mainstream methods.

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mentioning

confidence: 99%

A multivariate natural gas load forecasting method based on residual recurrent neural network

Ni,

Yang,

Qin

et al. 2023

Electronics Letters

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show abstract

Application of forecasting strategies and techniques to natural gas consumption: A comprehensive review and comparative study

Tian,

Shao,

Bian

et al. 2024

Engineering Applications of Artificial Intelligence

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Urban Gas Load Forecasting Based on Time Series Methods

Cited by 2 publications

References 13 publications

A multivariate natural gas load forecasting method based on residual recurrent neural network

A multivariate natural gas load forecasting method based on residual recurrent neural network

Application of forecasting strategies and techniques to natural gas consumption: A comprehensive review and comparative study

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