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A novel kernel ridge grey system model with generalized Morlet wavelet and its application in forecasting natural gas production and consumption

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  • Ma, Xin
  • Deng, Yanqiao
  • Ma, Minda

Abstract

Natural gas production (NGP) and consumption (NGC) always exhibit high nonlinearity, posing challenges for accurate small-sample forecasting. In this work, a novel kernel ridge grey system model with an extended parametric Morlet wavelet (GMW-KRGM) is proposed by integrating the kernel ridge regularization and grey system modelling within a partially linear regression framework and trained by the conjugate gradient method to mitigate the ill-posed problem. Besides, a weighted multi-objective optimization strategy is designed for model hyperparameter optimization and solved by the grey wolf optimizer (GWO). Six real-world NGP and NGC forecasting cases are carried out and empirical results demonstrate that the proposed GMW-KRGM model with optimal hyperparameters solved by GWO always yields superior forecasting performance than the other 2 machine learning models and 7 conventional grey system benchmarks with out-of-sample mean average percentage error (MAPE) improved in 7.4245%–91.8392% and 14.7303%–42.67% on average, respectively and yields more precise forecasting accuracy with fast and stable convergence than the other 5 optimization algorithms with improved MAPE range from 9.5608% to 48.2584%, indicating that the proposed model holds the capability to effectively deal with the nonlinear complex system and has great potential in nonlinear small sample forecasting.

Suggested Citation

  • Ma, Xin & Deng, Yanqiao & Ma, Minda, 2024. "A novel kernel ridge grey system model with generalized Morlet wavelet and its application in forecasting natural gas production and consumption," Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:energy:v:287:y:2024:i:c:s0360544223030244
    DOI: 10.1016/j.energy.2023.129630
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