With the development of electric power industry marketization, the importance of electricity price forecasting has been gradually highlighted. Because of the randomness of electricity price and the good generalization ability of neural network to deal with various non-linear problems, the Back Propagation (BP) neural network algorithm is widely used to predict electricity price. However, BP neural network has some disadvantages such as slow convergence rate and easy to fall into local optimum, so this paper improves the BP neural network prediction algorithm based on Genetic Algorithm (GA). The traditional BP neural network is easy to get the error signal into local minima, and the genetic algorithm can solve the problem by optimizing the weights and thresholds of BP neural network. This paper chooses the electricity price of electricity market in Australia as an example, uses the genetic algorithm and BP neural network model to solve the actual operation, realizes the cause of the final error, and verifies the superiority of BP neural network based on genetic algorithm compared with the traditional BP neural network.

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