Research And Application Of Genetic And Tabu Search Neural Network In Short-load Forecasting

Load forecasting of electric power system has all-important effect on power system, such as operation, control and plan. The prediction accuracy has direct influence on economy benefits of the grid and power plants. Applying neural network to load forecasting of electric power system has become prevalent. The BP neural network is easy to stick in local optimization and has slow convergence speed. It takes much time and effort to determine the structure of neural network, which makes the neural network have the disadvantages of low prediction precision and worse applicability. This paper proposed the mixed Genetic Tabu algorithm(GATS). The Genetic Tabu neural network model and the improved genetic tabu neural network model were formed, which were trained by GATS. Applying them to the short-term load forecasting, the results were that the two models have higher prediction precision comparing with normal genetic algorithm and tabu search, especially, the improved genetic tabu neural network model obtained the perfect structure and the optimum weights at the same time, forever, it posessed better applicability of model.