Estimation of the performance of photovoltaic cells by means of an adaptative neural fuzzy inference model

Mateo Romero, Héctor Felipe; Carbonó de la Rosa, Mario Eduardo; Hernández Callejo, Luis; González Rebollo, Miguel Ángel; Cardeñoso Payo, Valentín; Alonso Gómez, Víctor; Martínez Sacristán, Óscar; Gallardo Saavedra, Sara

doi:10.1007/978-3-031-52517-9_12

Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/65998

Título

Estimation of the performance of photovoltaic cells by means of an adaptative neural fuzzy inference model

Año del Documento

2024

Documento Fuente

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

Resumen

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.

ISSN

1865-0929

Revisión por pares

DOI

10.1007/978-3-031-52517-9_12