Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks

TL;DR

This paper presents a Physics-Informed Neural Network approach to solve a complex nonlinear energy supply-demand system, demonstrating comparable accuracy to traditional methods while offering continuous domain solutions, despite high computational costs.

Contribution

The study introduces a novel PINN-based method for solving nonlinear energy systems, highlighting its ability to provide continuous solutions and handle complex differential equations effectively.

Findings

PINNs achieve solutions comparable to RK45 method.

Neural networks provide continuous domain solutions.

The approach requires significant computational resources.

Abstract

Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed Neural Networks (PINNs) to solve the nonlinear energy supply-demand (ESD) system. We design a neural network with four outputs, where each output approximates a function that corresponds to one of the unknown functions in the nonlinear system of differential equations describing the four-dimensional ESD problem. The neural network model is then trained and the parameters are identified, optimized to achieve a more accurate solution. The solutions obtained from the neural network for this problem are equivalent when we compare and evaluate them against the Runge-Kutta numerical method of order 4/5 (RK45). However, the method utilizing neural networks is considered a modern and promising approach, as it effectively exploits the superior computational power of advanced computer systems, especially in solving complex problems. Another advantage is that the neural network model, after being trained, can solve the nonlinear system of differential equations across a continuous domain. In other words, neural networks are not only trained to approximate the solution functions for the nonlinear ESD system but can also represent the complex dynamic relationships between the system's components. However, this approach requires significant time and computational power due to the need for model training.