Research on the Effective Asset Prediction Model of Power Grid Enterprise Based on Particle Swarm Algorithm

Under the background of cost supervision and examination and electricity price verification, the operation and management of power grid enterprises are complicated affected. The scale of effective assets directly determines the verification result of electricity price, and further plays a decisive role in the actual operation of power grid enterprises. Therefore, the reasonable formulation of the transmission and distribution price of power grid enterprises is very important, especially the accurate prediction of effective assets plays a key role in the price verification. This study presents an effective asset prediction model combining particle swarm algorithm with neural network, aiming to optimize the accuracy and reliability of prediction. By analyzing the historical data, the key features related to the asset type, the investment scale and the asset verification ratio are extracted, and the structure and parameters of the neural network are optimized by using the particle swarm algorithm. The experiment shows that the optimized model significantly improves the accuracy of effective asset prediction and has strong application and promotion value.