The Power Load Prediction Based on Improved Genetic Neural Network

Dong Dai, Lin-Chao Ma


There exist nonlinear and high redundancy between the power load factors, and the traditional methods in neural network can not eliminate the redundancy in the prediction data and capture the nonlinear characteristics, resulting in lower prediction precision of the power load. In order to improve the prediction precision of the power load, a power load prediction method based on improved genetic algorithm optimization neural network (IGA-BP) is put forward. First, the power load is reconstructed using the correlation function, and then performed the normalization processing. Secondly, the power load training sample is input into the BP neural network for learning, and then the initial connection weights and thresholds of the BP neural network are selected using the improved genetic algorithm. Finally, the power load prediction model is established. Simulation results show that the improved neural network can reflect the trend of complex non-linear power load, achieve higher power load fitting and prediction precision, and is an effective load prediction method for power system.



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