Application Research Of Wind Power Short-Term Prediction Based On Artificial Neural Network

With the non-renewable fuels are depleting and environmental pollution is increasing, the development of wind energy and other clean, renewable resources worldwide attention. However, wind energy is a low energy density of resources and its own volatility and stability, and is proportional to the cube of the output power of the wind turbine and wind speed, wind turbine output power with a certain degree of volatility and instability sex is inevitable. The unstable wind integration will have the stable operation of the power grid, power system security and power quality guarantee enormous challenges. Wind power prediction research is an effective way to solve this problem, a high level of practical significance.In this paper, the nonlinear prediction most widely used BP neural network and RBF neural network discussed wind farm power prediction modeling, and the use of the Northwest province the wind farm the historical operating unit data and NWP weather forecast data measured and defect analysis. On this basis, the particle swarm algorithm to optimize the BP neural network research strategy. Measured the effect of particle swarm optimization BP neural network has higher prediction accuracy and application value. Finally, under the guidance of the software engineering thinking to object-oriented programming principles of thinking, the use of J2EE technology platform software design and development of the wind farm power prediction system. In this paper, the actual production data of the number of wind farms in the northwest province power prediction system based on the system prediction error and practicality. The results of the analysis show that the system has higher prediction accuracy and practical value.