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Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM

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  • Zeng, Huibin
  • Shao, Bilin
  • Dai, Hongbin
  • Yan, Yichuan
  • Tian, Ning

Abstract

An accurate prediction on natural gas load is always a guarantee of a safe and reliable operation of the natural gas pipeline network system, however, natural gas daily load variations are instability and fluctuation. Therefore, accurate prediction fluctuation loads can be challenging. Concerning that many influencing factors of natural gas daily load are non-linear and time-varying, a combined prediction model combining GARCH family models, CatBoost algorithm, CNN and LSTM is proposed in this paper. The model combines two important techniques. The first technique is to visualize the fluctuation of the daily load of natural gas through the classical GARCH family model. and characterize the fluctuation through parameters. The second one is a new gradient boosting algorithm that can take into account factors such as the parameters of the GARCH family of models and the meteorological environment, and screen for suitable and important prediction features. It also combines CNN with LSTM to predict natural gas daily load with large fluctuations and calculates 95% confidence intervals. The experimental results show that the natural gas load prediction based on GARCH family-CatBoost-CNNLSTM is reduced by an average of 26.555%, 30.892%, and 26.283% in the three relative evaluation indicators of RMSE, MAE and MAPE, and an average increase of 0.914% in R2 compared with other single or combined control models such as LSTM, CNN and MLP. The model effectively combines the advantages of econometric methods, machine learning algorithms, deep learning algorithms and other techniques, as a result, it can be better applied to the prediction of fluctuation loads.

Suggested Citation

  • Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222030110
    DOI: 10.1016/j.energy.2022.126125
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    1. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. Huseyin Cagan Kilinc & Iman Ahmadianfar & Vahdettin Demir & Salim Heddam & Ahmed M. Al-Areeq & Sani I. Abba & Mou Leong Tan & Bijay Halder & Haydar Abdulameer Marhoon & Zaher Mundher Yaseen, 2023. "Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3699-3714, July.
    3. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    4. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).

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