**4. Conclusions**

The main objective of this study was to evaluate the applicability of the GDAS sflux numerical weather model as a replacement for in situ weather measurements to model the power outputs of a photovoltaic system. Three training and testing scenarios, with different combinations of monitoring and GDAS weather data, were used to feed and evaluate the performance of one prediction model using a multilayer perceptron ANN algorithm. Solar irradiation and air temperature were the main input variables, while PV power production was the only predicted output.

Bias errors on individual days tended to be compensated when considering more complete temporal samples. This happened both on the weather inputs and the PV power outputs.

Mean nRMSE values of 2.9% and 9.9% on PV outputs were achieved for the most representative testing sample in the first and second scenarios, respectively. A comparison between those values led to the conclusion that most of the power prediction errors were due to the approximate nature of the GDAS solar irradiation data. However, the 100.00 W/m2 mean RMSE error achieved for this weather variable was in accordance with other solar irradiation forecast methodologies included in the bibliography. The neural network model used was shown to model the real power system with solid accuracy.

An analysis of the second scenario indicated that the GDAS sflux product is a reliable source of weather data for forecasting future PV power outputs, when an ANN model built with past in situ weather measurements is already available. The analysis of the third scenario, on the other hand, showed that even when said historic dataset of local weather measurements is not available, GDAS data can be effectively used to train the ANN model, with a minimal loss in the accuracy of PV power predictions.

Less than 10% mean nRMSE errors in PV power outputs were achieved for both the second and third scenarios. A comparison with other relevant studies showed that the errors in the photovoltaic power predictions for all scenarios in this study were analogous to those presented in the solar forecasting literature. The use of GDAS weather data in combination with ANN algorithms makes it possible to predict PV power with a performance that matches or even outmatches other PV forecast methods.

Future research expanding this work could focus on tackling some aspects which were not fully analysed in the present study. The influence of other weather variables could be studied (like wind effects on the cooling of the solar modules, or rainfall removing possible depositions of fine dust and dirt), if reference measured data were available for said variables. Second order B-splines were used here to interpolate weather values for the hours when GDAS data were not generated, but other temporal interpolation methods could be tested. Finally, the performance of the GDAS model could be tested against other NWP models, like a high resolution version of the Global Forecast System.

In conclusion, the present study shows that the GDAS sflux numerical weather model is a reliable source of weather data for photovoltaic power prediction when combined with Artificial Neural Network algorithms. Estimative data from this numerical model can be fed into an existing ANN model, already trained with local weather measurements, or can entirely replace said measurements and be used to train the model when historical local weather data are not available.

**Author Contributions:** Conceptualization, J.L.G., A.O.M. and L.F.G.; Data curation, J.L.G. and A.O.M.; Formal analysis, J.L.G. and A.O.M.; Funding acquisition, E.G.Á. and J.A.O.G.; Investigation, J.L.G. and A.O.M.; Methodology, J.L.G., A.O.M. and F.T.P.; Project administration, E.G.Á.; Resources, L.F.G. and J.A.O.G.; Software, J.L.G., A.O.M. and F.T.P.; Supervision, E.G.Á. and J.A.O.G.; Validation, J.L.G., A.O.M. and F.T.P.; Visualization, J.L.G., A.O.M. and L.F.G.; Writing—Original draft, J.L.G. and A.O.M.; Writing—Review & editing, F.T.P. and L.F.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This investigation article was partially supported by the University of Vigo through the grant Convocatoria de Axudas á Investigación 2018: Axudas Predoutorais UVigo 2018 (grant number 00VI 131H 641.02). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government through the grant Ayudas para la Formación de Profesorado Universitario: Convocatoria 2017 (grant number FPU17/01834). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government by means of the SMARTHERM (Project: RTI2018-096296-B-C2).

**Acknowledgments:** The authors would like to than to the National Centres for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration (NOAA) for providing free public access to their historic repositories of GDAS outputs through the Archive Information Request System (AIRS).

**Conflicts of Interest:** The authors declare no conflict of interest.
