High-voltage line loss prediction based on improved EO-BP neural network

Abstract

Aiming at the problem of low accuracy of high voltage line loss prediction, a line loss prediction model is proposed based on improved BP neural network and Equalization optimizer (EO) algorithm. Firstly, in order to improve the optimization ability of EO algorithm, a variety of chaotic mapping relations is used to initialize the population to increase the population diversity, then the global search ability could be improved. At the same time, the EO algorithm is improved by using the natural selection probability jump strategy, so that the model could jump out of the local optimization according to the probability and converge to the global optimal solution. Secondly, the improved EO algorithm is used to optimize the weight and bias of BP neural network, and the prediction effect of BP neural network for high voltage line loss is improved. Finally, the experimental results show that the proposed line loss prediction model has the highest prediction accuracy compared with regression model, BP neural network model, simulated annealing optimized BP neural network model and EO optimized BP neural network model.

Keywords

• line loss prediction
• chaotic mapping
• natural selection probability jump strategy
• equilibrium optimizer algorithm
• neural network