Multi-Step Natural Gas Load Forecasting Incorporating Data Complexity Analysis with Finite Features.

Area of Science:

• Complex Systems Analysis
• Energy Forecasting
• Data Science

Background:

• Data complexity, including self-similarity and long-term memory, impacts forecasting model accuracy.
• Natural gas load data exhibit inherent complex characteristics that challenge traditional forecasting methods.
• Understanding and quantifying these features are crucial for developing robust predictive models.

Purpose of the Study:

• To quantify and evaluate the complexity features of natural gas load data.
• To develop an advanced multi-step-ahead forecasting model integrating data decomposition and ensemble deep learning.
• To incorporate identified complexity features and meteorological factors into the forecasting framework.

Main Methods:

• Quantification of complexity using fractal dimension, Hurst exponent, sample entropy, and maximum Lyapunov exponent.
• Feature screening and meteorological factor integration using eXtreme Gradient Boosting (XGBoost).
• Data decomposition via variational mode decomposition (VMD) and long-term dependency modeling with gated recurrent unit (GRU).

Main Results:

• The proposed XGBoost-VMD-GRU model demonstrated superior forecasting performance compared to other methods.
• Achieved high R-squared values: 0.9922 (1-step), 0.9860 (3-step), and 0.9679 (6-step).
• The integration of complexity features significantly enhanced prediction accuracy and robustness.

Conclusions:

• The study successfully integrated data complexity analysis into a decomposition-based forecasting framework.
• The developed model offers a novel and effective approach for natural gas load forecasting.
• This research provides innovative insights for improving the accuracy and reliability of complex system predictions.