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Short term power load sample is highly variable, and the influence factors are not determined, the data sample is little. In view of the characteristics of the load, we combine the Grey Theory and Elman neural network to predict the short-term power load. Because the gray neural network convergence is slow, We introduce the genetic algorithm to the gray Elman neural network optimization, and propose the genetic algorithm to optimize the gray Elman neural network algorithm, the genetic algorithm to optimize the gray Elman neural network algorithm is applied to short-term load forecast. Experimental results show that the prediction accuracy is improved. The algorithm achieves fast convergence, and it is feasible and effective.

A Short Term Load Forecasting Algorithm Based on Gray Elman Neural Network and Genetic Algorithm