Research And Design Of Photovoltaic Power Forecasting System

Research on photovoltaic power generation forecast is of great help to ensure stable operation and improve the photoelectric absorption capacity of the power grid.The research can also reduce the economic losses caused by photovoltaic power stations due to power rationing,and improve the efficiency of power station operation and management.However,there are several problems in the design and application of photovoltaic power prediction systems.First,the model cannot be updated in time according to the field conditions,which leads to the decline of prediction accuracy.Second,there is no special design for some extreme weather,resulting in the lack of prediction ability under extreme weather.Third,some photovoltaic power plants lack meteorological observation stations onsite and cannot obtain real-time updated weather forecasts.In this case,there is little relevant research literature on the ultra-short-term prediction of the next four hours.Solving these problems is of great significance to improve the accuracy of photovoltaic power forecasts.Therefore,based on the historical operation data of photovoltaic power stations provided by the cooperative research institute,this paper conducts an in-depth study on the related issues of photovoltaic power prediction.The main works completed in this paper are as follows:(1)The overall design framework of the system is determined.The operation principle of a largescale photovoltaic power generation system is investigated.The demand for a photovoltaic power prediction system is analyzed.The performance index of prediction is determined.And the structure and main functions of the system are designed.Finally,the overall operation process of the system is described,and the follow-up research content is clarified.(2)The existing data are preprocessed and analyzed before modeling.The isolated forest algorithm is used to clean the historical data of the photovoltaic power station.Then the influence factors of PV power output are analyzed.After analyzing the meteorological data under different weather conditions,the idea of hierarchical division is used to divide the weather types into sunny,cloudy,and rainy with as few features as possible.The first layer uses the improved k-means algorithm,and the second layer uses the second-order difference method.(3)The short-term power prediction model of photovoltaic power generation is designed.Aiming at the problem that the current short-term prediction model can not adapt to the changes of field conditions and environment,which leads to the decrease of prediction accuracy,an online learning and updating photovoltaic short-term power prediction model is designed based on the OSELM algorithm.The model has achieved good results in three weather types: sunny,cloudy,and rainy.The short-term prediction r RMSE are 2.98%,5.32% and 4.09%.Aiming at the influence of haze weather on photovoltaic power prediction,a pre-correction model for irradiance is designed with PM2.5 as an example.The experimental results show that the prediction error can be reduced by about 5% in severe haze weather.(4)The ultra short term power prediction model of photovoltaic power generation is designed.The advantages and disadvantages of similar day model,power series-based model,and error seriesbased model are analyzed.Aiming at the problem that the traditional KNN algorithm takes a long time to process large quantities of data,a similar day prediction method based on ANNOY algorithm is proposed.It improves efficiency while ensuring accuracy.To meet the demand of four-hour ultra short term forecasting without real-time weather forecast,an error series ultra-short-term prediction model based on GRU is proposed,which is different from the traditional methods of direct power series prediction,to ensure the overall accuracy and stability of the prediction.Finally,the ultra-shortterm power prediction model designed combines the advantages of the three models and avoids the disadvantages of each model.The prediction accuracy of step 16 on the experimental data set reaches96.20%.(5)The development and trial operation of the photovoltaic power generation prediction system is completed.The system software is developed based on JAVA and Python,and realizes the coexistence of the two languages through the method called at run time.The system has good manmachine interaction and has achieved good operation effect under the data provided by several photovoltaic power stations.In this paper,the system design,data processing,modeling and prediction are carried out by investigating the actual operation demand of photovoltaic power stations.The problems of short-term model self-adaptive updating,power prediction in extreme weather and ultra-short-term power prediction in the absence of meteorological observation stations are mainly solved.It is of great significance to improve the prediction accuracy of photovoltaic power output,improve the utilization efficiency of power generation grid-connection and maintain the development trend of high-speed growth of the photovoltaic industry.