Differential equation modelling in dynamic modelling and prediction of power index data

This paper combined differential equation models to study the dynamic modelling and prediction of power index data. Firstly, it collected electricity data from a certain power company from 1 June to 15 June 2023, and selected electricity index data to construct a partial differential equation model. The boundary and initial conditions were defined, and nonlinear effects and coupling relationships were established. Then, the least squares method and gradient descent algorithm were combined to estimate the parameters of the partial differential equation model. It then solved the model using the fourth-order Runge-Kutta method. Finally, based on the results of differential equations, this paper introduced the LSTM model for the dynamic prediction of power indicators. It demonstrates that the combined differentiable equation model-LSTM method performs well in power indicator prediction, enhances prediction accuracy, and guarantees steady power system operation. It also demonstrates a good capacity for capturing data changes in indicators.