Optimization of Complex Systems Using Neural Networks: A Case Study on Power Systems

Abstract

The optimization of complex systems is a critical challenge due to the numerous interacting factors and variables involved. Traditional optimization methods, such as gradient descent and genetic algorithms, often fall short in addressing these complexities. This study explores the application of artificial intelligence (AI) algorithms combined with neural networks (NN) for optimizing complex systems, specifically focusing on power systems. A fusion NN model is designed, incorporating feature extraction and data preprocessing methods tailored to the power system's characteristics. Experimental results demonstrate the model's high accuracy and applicability, meeting practical requirements for power system optimization. The findings suggest that this approach can be extended to other complex systems, such as transportation, logistics, and industrial control, enhancing AI's contribution across various fields.