Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand.Although numerical weather prediction(NWP)mo...Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand.Although numerical weather prediction(NWP)models can forecast solar radiation variables,they often have significant errors,particularly in the direct normal irradiance(DNI),which is especially affected by the type and concentration of aerosols and clouds.This paper presents a method based on artificial neural networks(ANN)for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts(ECMWF)and the Copernicus Atmospheric Monitoring Service(CAMS),respectively.Two ANN models were designed:one uses as input the predicted weather and aerosol variables for a given instant,while the other uses a period of the improved DNI forecasts before the forecasted instant.The models were developed using observations for the location of´Evora,Portugal,resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2,MAE and RMSE of 0.0646,21.1 W/m^(2)and 27.9 W/m^(2),respectively.The model was also evaluated for different timesteps and locations in southern Portugal,providing good agreement with experimental data.展开更多
基金funded by National funds through FCT-Fundaçäao para a Ciência e Tecnologia,I.P.(projects UIDB/04683/2020 and UIDP/04683/2020)support of FCT-Fundaçäao para a Ciência e Tecnologia through the grant with reference SFRH/BD/145378/2019.
文摘Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand.Although numerical weather prediction(NWP)models can forecast solar radiation variables,they often have significant errors,particularly in the direct normal irradiance(DNI),which is especially affected by the type and concentration of aerosols and clouds.This paper presents a method based on artificial neural networks(ANN)for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts(ECMWF)and the Copernicus Atmospheric Monitoring Service(CAMS),respectively.Two ANN models were designed:one uses as input the predicted weather and aerosol variables for a given instant,while the other uses a period of the improved DNI forecasts before the forecasted instant.The models were developed using observations for the location of´Evora,Portugal,resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2,MAE and RMSE of 0.0646,21.1 W/m^(2)and 27.9 W/m^(2),respectively.The model was also evaluated for different timesteps and locations in southern Portugal,providing good agreement with experimental data.
