Pereira, S., Abreu, E.F., Iakunin, M., Cavaco, A., Salgado, R. and Canhoto, P., 2022: Method for solar resource assessment using numerical weather prediction and artificial neural network models based on typical meteorological data: Application to the south of Portugal. Solar Energy, 236, 225-238, https://doi.org/10.1016/j.
In this work a method for regional solar resource assessment based on numerical weather prediction (NWP) and artificial neural network (ANN) models is presented. The method was developed using typical meteorological and solar radiation data and applied to the location of Évora, Portugal with the goal of assessing solar global horizontal (GHI) and direct normal (DNI) irradiations with 1.25 km of horizontal resolution in the south of Portugal. The NWP model used was the research model Meso-NH and a site adaptation model was developed based in ANNs and using as inputs the simulated meteorological variables from Meso-NH and aerosol data from Copernicus Atmospheric Monitoring Services (CAMS) for the observation site. The resulting annual relative mean bias errors for GHI and DNI at Évora and typical meteorological year are of 0.55 % and 0.98 %, respectively, while the values for the original Meso-NH simulations are of 8.24 % for GHI and 31.71 % for DNI. The developed site adaptation model is applied to the region for the purpose of solar radiation assessment and validated using data from a network of solar radiation measuring stations scattered throughout the south of Portugal, showing relative mean bias errors of 2.34 % for GHI and 3.41 % for DNI, while the original Meso-NH simulations presents relative mean bias errors of 8.50 % and 29.54 %, respectively. These results allowed the generation of improved solar resource availability maps which are a very useful tool in solar resource assessment, the study of shortwave radiative climate, as well as project planning and solar system design and operation.