RT info:eu-repo/semantics/article
T1 Applications of Artificial Intelligence to Photovoltaic Systems: A Review
A1 Mateo Romero, Héctor Felipe
A1 González Rebollo, Miguel Ángel
A1 Cardeñoso Payo, Valentín
A1 Alonso Gómez, Victor
A1 Redondo Plaza, Alberto
A1 Moyo, Ranganai Tawanda
A1 Hernández Callejo, Luis
AB This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/65995
UL https://uvadoc.uva.es/handle/10324/65995
LA spa
NO Mateo Romero, H.F.; González Rebollo, M.Á.; Cardeñoso-Payo, V.; Alonso Gómez, V.; Redondo Plaza, A.; Moyo, R.T.; Hernández-Callejo, L. Applications of Artificial Intelligence to Photovoltaic Systems: A Review. Appl. Sci. 2022, 12, 10056. https://doi.org/10.3390/app121910056
DS UVaDOC
RD 17-may-2024