RT info:eu-repo/semantics/article
T1 Estimation of the performance of photovoltaic cells by means of an adaptative neural fuzzy inference model
A1 Mateo Romero, Héctor Felipe
A1 Carbonó de la Rosa, Mario Eduardo
A1 Hernández Callejo, Luis
A1 González Rebollo, Miguel Ángel
A1 Cardeñoso Payo, Valentín
A1 Alonso Gómez, Víctor
A1 Martínez Sacristán, Óscar
A1 Gallardo Saavedra, Sara
AB This paper presents an Adaptive Neuro-fuzzy Inference System capable of predicting the output power of photovoltaic cells using their electroluminescence image and their IV curve. The input consists of 3 different features: the number of black pixels, grey pixels and white pixels. ANFIS combines the learning capabilities of Artificial Neural Networks with the comprehensible rules of Fuzzy Logic, being optimal for this problem, as demonstrated by the metrics of MAE of 0.064 and MSE of 0.009, which are better than the performance of other tested methods such as Support Vector Machines or Linear Regressor.
SN 1865-0929
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/65998
UL https://uvadoc.uva.es/handle/10324/65998
LA spa
NO Mateo-Romero, H.F. et al. (2024). Estimation of the Performance of Photovoltaic Cells by Means of an Adaptative Neural Fuzzy Inference Model. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, vol 1938. Springer, Cham. https://doi.org/10.1007/978-3-031-52517-9_12
DS UVaDOC
RD 18-dic-2024