Design And Research Of Photovoltaic Prediction Scheme Based On Hybrid Prediction

Photovoltaic power generation technology has received extensive attention due to its clean,efficient and safe features.Photovoltaic power plants have been built in large numbers.However,since photovoltaic power generation is greatly affected by environmental factors,the discontinuity,instability and volatility of its performance have adversely affected the system debugging and power grid planning.Therefore,the establishment and development of the model of photovoltaic power prediction system is of great significance.At present,the research involving prediction systems mainly focuses on the construction of prediction models,and lacks the overall scheme design of the prediction system.Many problems need to be further improved.Therefore,this paper takes the design of the prediction system as the research focus,carries out the theoretical research work of the hybrid prediction model,and designs the software function and interface of the prediction system.Main tasks as follows:(1)Affected by the environment,weather and other aspects,the power sequence acquisition process contains a lot of noise,which affects the accuracy of the prediction model.In order to further obtain the use value from a large amount of data,this paper uses the wavelet transform to reduce the noise of the power input sequence,and verify the reliability through simulation.Correlation analysis was performed on meteorological signals and power sequence signals.(2)In order to further improve the prediction accuracy,this paper proposes a hybrid prediction method based on SE-IPSO-LSSVM-iteration error correction.Firstly,the sample entropy theory is used to quantize the weather type as the eigenvector input prediction model to participate in the prediction.Then the least squares support vector machine is used to predict the power and error respectively.Finally,the error compensated power value is taken as the final prediction result.At the same time,the weights and thresholds of the particle swarm optimization algorithm are improved,the inheritance and mutation ideas are added,and the parameters of the support vector machine are optimized by using the improved particle swarm optimization algorithm.(3)Taking the measured data of a distributed photovoltaic power plant as an example,based on Matlab,a prediction model is built to predict the photovoltaic power generation in the next 1 minute.At the same time,three similar prediction models,namely,similar day-LSSVM,SE-LSSVM,and similar-IPSO-LSSVM,were established to test the test samples.Through simulation analysis,the superiority of this method in photovoltaic power prediction is compared with the traditional method.Finally,the model is evaluated by root mean square error and average relative error respectively,which verifies that the model has good universality.(4)The function of the PV forecasting system software was designed,and the software interface was designed using VB.