SILVA, C. C.; http://lattes.cnpq.br/1170293319687757; SILVA, Cleiza Cristiano.
Resumen:
In recent years, solar energy has gained significant relevance due to its renewable nature,
considered one of the sources with almost no impact on the environment and economically viable.
Due to the geographic conditions of the NEB, the use of photovoltaic energy becomes a great
option for the diversification of energy sources. In this context, this research is motivated by the
analysis of the application of Deep Learning techniques, known as "Deep Learning" DL, for the
prognosis of solar energy generation in the NEB, aiming to expand knowledge and facilitate study
applications in the area. For this, ERA5 reanalysis data was used from 2009 to 2019, including
information such as SSRD, GHI, T2M, TP and TCC, in addition to data from INMET of Rg. Power
generation data, including GEV, CGE and CI, were obtained from ONS. When analyzing
meteorological data, correlations were observed between T2M, Rg and other meteorological
variables. With the efficiency data in solar energy generation, it was identified that the plants with
the highest IC potential are concentrated in Piauí and Bahia, regions where the GHI has high hourly
average values, reaching approximately 200 Wh/m². These regions also present higher T2M and
SSRD values, and lower TCC and TP values. The efficiency of the forecast models was examined
in more detail for the Bom Jesus, Horizonte, Nova Olinda, Rio Alto and Sol do Futuro Complexes,
located in Bahia, Piauí, Paraíba and Ceará, highlighting the GRU method and combinations that
involve it, such as superior in terms of RMSE and MAE metrics, maintaining consistency for
different step sizes. Validation, through comparison of predicted with observed data, reinforced
that the GRU, or combinations that involve it, present more effective results in the prediction task
for the data sets studied.