Artificial neural network applied to prediction of electricity generated by Grid connected photovoltaic systems

Author

de Vasconcelos, Fillipe M. ; de Saraiva, Filipe O. ; Bernardes, W.M.S. ; Mazzini, Ana Paula ; Pinho Almeida, Marcelo

Author_Institution

Sao Carlos Sch. of Eng., Dept. of Electr. & Comput. Eng., USP, Sao Carlos, Brazil

fYear

2013

fDate

15-17 April 2013

Firstpage

1

Lastpage

6

Abstract

This paper applies Artificial Neural Network to predict the amount of energy generated by a Grid Connected Photovoltaic System installed at the Institute of Electrotechnic and Energy of University of São Paulo (IEE/USP). Irradiance, back cell temperature and power data were collected during the period of one year. This methodology allows performing an analysis of the production of Grid Connected Photovoltaic Systems and the commercialization of the energy generated. Finally, the methodology was validated comparing relative error between measured data and estimated data.

Keywords

neural nets; photovoltaic power systems; power grids; power system interconnection; Institute of Electrotechnic and Energy; University of São Paulo; artificial neural network; back cell temperature; electricity prediction; grid connected photovoltaic systems; irradiance; power data; Artificial neural networks; Educational institutions; Electricity; Mathematical model; Photovoltaic systems; RNA; Temperature measurement; Artificial Neural Network; Grid Connected Photovoltaic Systems; Prediction of Electricity Generation;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Smart Grid Technologies Latin America (ISGT LA), 2013 IEEE PES Conference On

Conference_Location

Sao Paulo

Print_ISBN

978-1-4673-5272-7

Type

conf

DOI

10.1109/ISGT-LA.2013.6554453

Filename

6554453