Study On Short-Term Forecasting Method Of PV Power Generation With Influence Of Fog And Haze

The short-term forecast for photovoltaic power generation is particularly important for grid scheduling and operation.In recent years,large-scale continuous hazy weather in North China frequently occurs in our country,which has a significant impact on the output power of photovoltaic power plants in the region and poses a severe challenge to the dispatch and operation of the power grid.Haze treatment is a long-term process,so it is of great practical significance and application value to study the short-term power forecast of photovoltaic generation in haze weather.Based on the study of a variety of photovoltaic power prediction method on the basis of the first,through collecting a large amount of historical data,the analysis of the fog weather,concentration of PM10 and PM2.5 concentration under a variety of meteorological factors and the correlation of atmospheric aerosol optical depth(AOD),the PM2.5,PM10 concentration,humidity,and temperature with the highest correlation degree were selected as inputs.The AOD prediction model of the 1020 nm band and 440 nm band was established respectively by BP neural network.The improved Irradiance calculation model was used to calculate the irradiance,and then predict the output of photovoltaic cell;Secondly,considering the influence of fouling caused by smog and haze on the photovoltaic panels,the power decay rate of photovoltaic cells under different inclinations was tested and the decay rate of photovoltaic power under haze fouling was established by BP neural network model;Finally,the predicted PV cell output is introduced into the attenuation rate correction,and a short-term PV power forecasting method considering the influence of haze is proposed.Through experiments,the proposed AOD prediction model,attenuation rate prediction model and power prediction method are verified.Based on the above research,we can predict the effective photovoltaic power generation accurately through the analysis of specific examples and historical operation data and meteorological data of photovoltaic cells.The experimental results show that they have high prediction accuracy and are easy to implement.Verify the effectiveness of the method and model proposed in this paper.