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

## TL;DR

This paper introduces a new model combining genetic algorithms and machine learning to improve demand forecasting and inventory management for power distribution equipment.

## Contribution

The novel fusion model integrates genetic algorithms with machine learning for improved prediction and inventory optimization in power systems.

## Key findings

- The model reduces unit prediction error under load fluctuations and extreme weather.
- It achieves a 3.41% mean absolute percentage error and 0.942 coefficient of determination in load time series data.
- The model lowers inventory levels, total cost per unit, and redundant inventory ratio compared to existing methods.

## Abstract

To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated “prediction-optimization” model that combines genetic algorithm (GA) with machine learning methods. This method uses GA to intelligently screen key features and optimize model parameters. It dynamically integrates the prediction link with inventory decisions, alleviating the problem of multi-objective coupling imbalance in traditional fragmented optimization. Compared with a single machine learning or heuristic algorithm, this model significantly reduces the unit prediction error under load fluctuations and extreme weather scenarios. Verification of model performance based on The European Network of Transmission System Operators for Electricity (ENTSO-E) dataset shows that the model achieves good results in the prediction stage. For example, in load time series data, the mean absolute percentage error is 3.41%, and the coefficient of determination reaches 0.942. In the inventory optimization stage, the model reduces the average inventory level to 42.63, controls the total cost per unit equipment at 92.37, and lowers the redundant inventory ratio to 9.42%. Its comprehensive performance is better than that of Temporal Fusion Transformer (TFT) and Neural Basis Expansion Analysis for Time Series Forecasting (N-BEATS). This work provides theoretical models and empirical support for research in the field of typical equipment prediction and inventory optimization in intelligent power distribution systems, and has certain practical value and promotion significance.