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A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load

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Accurate annual power load forecasting is crucial for power system stability. This study introduces a novel grey model (GM (1,1)) with a variable-weight weakening buffer operator and background value optimization, improving forecasting precision.

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Forecasting Science

Background:

  • Annual power load forecasting is essential for power system planning and safe operation.
  • Traditional grey model GM (1,1) is widely used but can be improved for accuracy.
  • Existing buffer operators lack adjustable action intensity, limiting data pre-processing effectiveness.

Purpose of the Study:

  • To enhance the accuracy of annual power load forecasting.
  • To address the limitations of traditional weakening buffer operators in GM (1,1) models.
  • To propose a novel VWWBO-BVO-based GM (1,1) model optimized for forecasting.

Main Methods:

  • Dynamic pre-processing of historical power load data using Variable-Weight Weakening Buffer Operator (VWWBO) and Background Value Optimization (BVO).
  • Integration of Grey Relational Analysis (GRA) and Improved Gravitational Search Algorithm (IGSA) to optimize model parameters.
  • Construction of a GRA-IGSA integration algorithm to maximize grey relativity between simulated and actual values.

Main Results:

  • The proposed VWWBO-BVO-based GM (1,1) model demonstrates improved forecasting accuracy.
  • The GRA-IGSA optimization effectively determines optimal parameters for the buffer operator and background value.
  • Case studies validate the enhanced predictive performance of the integrated model.

Conclusions:

  • The VWWBO-BVO-based GM (1,1) model offers an optimized solution for annual power load forecasting.
  • Adjustable buffer operator action intensity significantly improves forecasting precision.
  • The GRA-IGSA integration algorithm provides robust parameter optimization for enhanced predictive accuracy.