We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

lib:43d192928a27423b (v1.0.0)

Authors: Francesco Bonanno,Giacomo Capizzi,Christian Napoli,Giorgio Graditi,Giuseppe Marco Tina
ArXiv: 1308.2375
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1308.2375v1


The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!