Photovoltaic Power Forecasting System Research

As a kind of clean renewable energy,solar energy has many advantages,such as rich resources,good industrialization foundation,obvious economic advantage,small environmental impact and so on.It has received more and more attention from all countries in the world.However,because of the uneven distribution of solar energy,the use of solar energy is easily affected by meteorological factors such as temperature and humidity,which makes the output power of solar power generation high and intermittent.Large scale solar power generation system will bring an unexpected impact on the safe and stable operation of large power grids.Therefore,the effective and accurate prediction of the output power of solar photovoltaic power generation for a period of time is convenient.It is convenient for the power dispatching department to arrange the traditional power generation and photovoltaic power generation plan,which can effectively reduce the impact of the solar photovoltaic power generation system on the large grid and ensure the smooth operation of the power supply system.In order to improve the accuracy of the prediction of the output power of the solar photovoltaic power generation and develop a practical power prediction system software,this paper studies the output power prediction algorithm of the solar photovoltaic power generation system,and develops a software for the photovoltaic power pretest system based on the JavaEE SSM framework.The data of Alice springs Solar Centre Desert Knowledge Australia No.thirty-fifth photovoltaic power station has been applied in engineering practice.The main work of this paper is as follows:(1)The principle and correlation analysis of photovoltaic power generation.The generation principle,mathematical model and influence factors of photovoltaic power generation technology are discussed.Through the correlation analysis of temperature,solar radiation,relative humidity,wind speed,wind direction and other historical meteorological data and historical output power,the related factors affecting the output power of solar photovoltaic power generation are determined.(2)The prediction method of photovoltaic power generation is studied.The output power prediction model based on the BP neural network algorithm is established.The output power prediction model based on the dual parallel process neural network algorithm and the output power prediction based on the dynamic domain particle swarm optimization(PSO)neural network algorithm based on the dynamic domain particle swarm optimization(PSO)neural network algorithm are established to improve and improve the prediction model of the output power based on the BP neural network algorithm.Model?The simulation study shows that the two prediction models based on the improved BP neural network have higher prediction accuracy than the original model,and the prediction accuracy of the BP neural network based on dynamic field particle swarm optimization(PSO)is higher and can meet the requirements of the engineering application.(3)The PV power forecasting system is developed.The system is analyzed in demand,the design principle of the system is clearly defined,the organizational structure and logical structure of the system are determined,and the functional modules such as user management,real-time monitoring,power prediction,statistical report,and equipment management are designed.Based on the JavaEE SSM framework,the PV power forecasting system software is implemented,and it is successfully released to the Ali cloud server based on the Linux system.Through the operation test,the system runs steadily and achieves the desired purpose.purpose.