摘要：With the increase in the use of renewable energy, especially the development and utilization ofsolar energy resources, accurate photovoltaic power generation prediction technology will help thepromotion of photovoltaic power generation. The amount of photovoltaic power generation depends onweather conditions, and it is easy to produce large fluctuations under different weather conditions. Its powergeneration has the characteristics of randomness, fluctuation and intermittency. In view of the shortcomingsof the traditional BP neural network prediction method, this paper proposes an improved artificial beecolony algorithm. The improved artificial bee colony algorithm is used to optimize the network parameterweights in the traditional BP algorithm, and the two algorithms are merged in global iteration. Based on thecharacteristics of training light intensity, weather, temperature and historical power value of photovoltaicoutput power,a photovoltaic power generation prediction model is established. The simulation results showthat the improved artificial bee colony algorithm in the neural network's photovoltaic power generationforecast improves the accuracy and convergence speed of the traditional BP neural network convergencesolution, and can provide more comprehensive information for grid power dispatch and control.
其他摘要：With the increase in the use of renewable energy, especially the development and utilization of solar energy resources, accurate photovoltaic power generation prediction technology will help the promotion of photovoltaic power generation. The amount of photovoltaic power generation depends on weather conditions, and it is easy to produce large fluctuations under different weather conditions. Its power generation has the characteristics of randomness, fluctuation and intermittency. In view of the shortcomings of the traditional BP neural network prediction method, this paper proposes an improved artificial bee colony algorithm. The improved artificial bee colony algorithm is used to optimize the network parameter weights in the traditional BP algorithm, and the two algorithms are merged in global iteration. Based on the characteristics of training light intensity, weather, temperature and historical power value of photovoltaic output power,a photovoltaic power generation prediction model is established. The simulation results show that the improved artificial bee colony algorithm in the neural network’s photovoltaic power generation forecast improves the accuracy and convergence speed of the traditional BP neural network convergence solution, and can provide more comprehensive information for grid power dispatch and control.
