Wind Power Prediction Based On Meteorological Data Of Wind Farms

Nowadays,the energy problem is becoming increasingly prominent,and it has gradually attracted the attention of many worlds and has become the focus.With the application and popularization of new energy technologies,the application of wind power generation has been gradually promoted.This technology is a relatively mature new energy power generation technology and can be used for large-scale development of a power generation method.However,due to the instability of wind energy,the wind power generation of wind farms poses severe challenges and has a serious impact on wind power generation.Therefore,the problem of wind power prediction is becoming increasingly prominent.After a large amount of real data simulation and practical application,the main solution to the problem of wind power generation is wind power prediction.Therefore,the main contents of the research work of this article are:First of all,this article is mainly based on historical meteorological data,the main reference data are ambient temperature,average wind speed,wind direction,based on these main historical meteorological data,so as to predict the wind power of the wind farm for a period of time in the future.According to the length of the forecast,it is mainly divided into three categories:(1)ultra-short-term wind power forecast;(2)short-term wind power forecast;(3)medium and long-term wind power forecast.In the ultra-short-term prediction,the pure BP neural network and the BP neural network optimized based on the quantum genetic algorithm are used to predict the wind power.Finally,the data is obtained through simulation.The latter method is more accurate in wind power prediction;In the short-term wind power forecasting,the pure BP neural network,the vertical and horizontal crossover algorithm are used to optimize the BP neural network,the particle swarm algorithm optimizes the BP neural network,the whale optimizes the BP neural network,and the entropy method optimizes the BP neural network to predict the wind power.It is more accurate to use the entropy method to optimize the BP neural network through simulation and to obtain the data.In the medium and long-term prediction,the gray model is mainly used to predict the wind power,and the accuracy of the prediction basically meets the requirements.Among the three forecasts of wind power for different lengths of time in the future,this article uses different methods to predict wind power,and these different methods are for three different modes.In the ultra-short-term wind power prediction in wind power generation,the method used is mainly based on the quantum genetic algorithm to optimize the BP neural network;in the short-term wind power prediction in the wind power generation,the method used is the entropy method;In the medium and long-term wind power prediction in wind power generation,the method adopted is to construct the gray model to predict the medium and long-term wind power.Finally,through experimental simulation,the required wind power predicted value is finally obtained.