Method for Predicting Output Power of Photovoltaic Power Stations Based on Improved Neural Networks
The output power of photovoltaic power generation systems is constrained by various factors,including weather,panel temperature,and sunshine duration,exhibiting significant random,fluctuating,and intermittent characteristics,poses a theat to the stable operation of power systems.Therefore,the proposed method for predicting the output power of photovoltaic power stations based on improved neural networks is of great significance for the scheduling,optimization,and energy management of power systems.Firstly,a correlation analysis was conducted on the factors affecting the output power of photovoltaic power stations.Two factors,total irradiation intensity and ambient temperature,were selected as inputs that are significantly correlated with the photoelectric conversion efficiency.Then,an improved neural network model was constructed,and finally,the prediction of the output power of the photovoltaic power station was achieved through this model.The results show that the improved neural network has been used to accurately predict the output of photovoltaic power generation systems.Improving the neural network model can basically reflect the general trend of output power,and can more accurately predict output power when dealing with complex and variable weather conditions and power plant operation status.
output power predictionphotovoltaic power stationsrenewable energyimproved neural networks
万方数据
维普
