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  1. Home
  2. Demand Forecasting And Inventory Optimization Of Distribution Equipment: A Fusion Model Based On Genetic Algorithm And Machine Learning.
  1. Home
  2. Demand Forecasting And Inventory Optimization Of Distribution Equipment: A Fusion Model Based On Genetic Algorithm And Machine Learning.

Related Experiment Video

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Demand forecasting and inventory optimization of distribution equipment: A fusion model based on genetic algorithm

Qingbo Tu1, Hongyang Zhang1, Weiwei Li2

  • 1Economic and Technological Research Institute, State Grid Shandong Electric Power Company, Jinan, China.

Plos One
|November 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an integrated "prediction-optimization" model using genetic algorithm (GA) and machine learning for intelligent power distribution systems. The model enhances equipment management by improving prediction accuracy and optimizing inventory, outperforming existing methods.

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Area of Science:

  • Electrical Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Intelligent and refined management of power distribution systems is crucial for equipment operation, maintenance, and emergency support.
  • Traditional fragmented optimization methods struggle with multi-objective coupling imbalances.

Purpose of the Study:

  • To propose an integrated "prediction-optimization" model for enhancing power distribution system management.
  • To improve equipment operation, maintenance, and emergency support through advanced modeling.

Main Methods:

  • Combines genetic algorithm (GA) for feature screening and parameter optimization with machine learning.
  • Dynamically integrates prediction with inventory decisions to address multi-objective imbalances.
  • Utilizes The European Network of Transmission System Operators for Electricity (ENTSO-E) dataset for verification.

Main Results:

  • Significantly reduces unit prediction error under load fluctuations and extreme weather compared to single methods.
  • Achieves a 3.41% mean absolute percentage error and 0.942 coefficient of determination in load time series prediction.
  • Reduces average inventory level to 42.63, unit equipment cost to 92.37, and redundant inventory ratio to 9.42%.

Conclusions:

  • The proposed model offers superior performance over Temporal Fusion Transformer (TFT) and Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS).
  • Provides theoretical models and empirical support for equipment prediction and inventory optimization in intelligent power distribution systems.
  • Demonstrates practical value and promotion significance for intelligent power grid management.