Research & Implementation Of Optimization Model For Wind Power Prediction Based On Large Data

At present, with the increasingly serious problem of environmental pollution and shortage of fossil fuels, the development and utilization of wind energy and other renewable energy sources have been paid more and more attention in the world. The development of wind power is the main form of wind energy utilization. Large scale use of wind energy into the gap, the volatility of the power system, not only to the security and stability of the power system to bring serious challenges, and increase the power grid system's rotation reserve capacity, indirectly brought about the overall cost of wind power generation. Therefore, it is necessary to predict the output power of the wind farm.In this paper, the wind power prediction technology based on large data is studied.First of all, the concept and characteristics of large data were sorted out, focusing on the process of large data processing and processing framework are described and analyzed,and the application of large data technology in the wind power industry, the feasibility of the note. Secondly, combined with the deep analysis of the influence factors of wind power prediction, the BP neural network and support vector machine model are studied,combining the advantages of both, the combination forecasting method is put forward. In contrast, the error rate is reduced, and the precision of prediction is improved effectively.Again, the use of the analysis of large data processing technology, combined with the establishment of the combination forecasting model to establish prediction model based on data of wind power and wind electric field of each typhoon turbine power prediction,eventually add up to get the output power of wind farm. Finally, the design and implementation of a wind power prediction system, and successfully running on the Guodian clean energy power company.