Reasearch On Wind Power Forecasting Based On Fuzzy C-Means Clustering And Case Based Reasoning

With the increasing pay attention to the ecological environment and the major deployment in the country's new energy strategy,wind power as a clean energy has been developed in a large-scale.Not only the global wind power installed capacity has increased year by year,and the wind power generation technology is increasingly mature.However the intermittent and volatility of wind make it uncontrollable,which is always a big problem in wind power technology.Therefore,it is urgent to improve the accuracy of wind power forecasting in wind farm.Taking wind power forecasting as the research object,the data collected from the wind tower are analyzed in order to explore and make a better use of wind data.A neural network model has high dependence on the training samples.The selection of training samples not only i includes the wide selection range to achieve stronger generalization ability,but also has higher prediction accuracy.Since the normal prediction model can hardly meet the requirement under the specied weather,this paper improves wind power prediction model using the case based reasoning technology.The main contents of this paper are:(1)The domestic and foreign status and development trend of wind power generation are summarized,and the significance and necessity of wind power forecasting are presented.This paper summarizes the research status of wind power forecasting and wind power forecasting model.(2)The wind data can not meet the requirements of the prediction sample,according to the national standard GB/T18709-2002 and the reference supplement.the wind data are checked,filled and corrected.Based on the measured data of a wind farm in Shanxi Province,the variation law of wind data is analyzed.The relationship between the wind power prediction error and the influence factors of wind power is analyzed.The necessary selection of model evaluation index is given.(3)In this paper,a neural network based on clustering algorithm is proposed to predict the wind power.Firstly,the clustering center of fuzzy C mean is determined by subtractive clustering.Using the actural data from the wind farm to accomplish the clustering analysis and neural network model training,not only considers the characteristics of the sample space,but also makes the model targeted,thus ensuring the generalization ability of the network while improving the prediction accuracy.(4)In order to improve the prediction accuracy of special weather and wind power,the forecasting effect of general model and wind data are analyzed.The prediction model of case based reasoning in special weather of wind power was established,the K nearest neighbor algorithm based on fuzzy clustering and particle swarm optimization is adopted for case retrieval,which improves the retrieval speed and accuracy.A large number of simulation experiments have been carried out using the actual data of a wind farm in Shanxi Province,The simulation results with GRNN,LSSVM and GABP model were compared.The prediction error has been improved in different degrees.Especially when the predicting data mutate in predicting the mutation data occurs,the effect is more obvious,so as to verify the validity of the method which provides a feasible method to solve the prediction of wind power under the special weather.