Design And Implementation Of Wind Power Prediction System Based On Integrated Learning

With the development of society,People’s energy demand is increasing.Traditional fossil fuels cannot satisfy social development and even damage the environment.Therefore,our country is paying more and more attention to the development of new energy.Wind energy has become one of the main green energy sources today because of its high efficiency,clean,and renewable advantages.Wind power has been developed in our country for many years,but it is rarely used in actual production.The main reasons are the random,volatile and intermittent nature of wind power,which can lead to unstable wind power output and challenges for large grid connections.Accurate forecasting of wind power is critical to enhance the stability of wind power and ensuring the safety of the grid.Based on the current development status of current wind power forecasting and existing issues,the research in this treatise can be divided into two aspects:(1)Based on the real data set of actual wind farm,a series of work such as feature analysis,feature combination,feature screening,etc.have been carried out on it,and based on integrated learning The XGBoost,Light GBM and Cat Boost in the algorithm respectively construct a single wind power prediction model,and then use Stacking for model fusion to improve the prediction effect.Finally,the optimal model is selected according to the experimental results to complete the wind power prediction model construction.(2)On this basis,this thesis carries out demand analysis and functional design of the wind power forecasting system,combined with the related technologies of the system development,to realize a wind power forecasting system.The wind power prediction system mainly includes four modules: wind power data push,wind power information display,wind power prediction,and wind power information statistics.In addition to the wind power prediction function,the system also provides the push,display,and statistics functions of wind power related data.The innovation in this article is that most current wind forecasts use a single model for forecasting.Take into account the problem of weak predictive generalization capabilities of traditional single models,in this thesis,we will improve the accuracy of prediction by using integrated learning algorithm and stacking model fusion,and design and implement wind forecasting system based on this,and expand the application direction of wind forecasting.Through the research in this article,on the one hand,it can promote the innovation of wind power forecasting models and provide new ideas for the research of wind power forecasting;on the other hand,the design and realization of a multi-functional wind power forecasting system can not only improve the utilization rate of wind energy,but also help related work.Staff improve work efficiency and promote the development of wind power forecasting systems from an application perspective.