Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/24098

Title: Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs
Authors: Pereira, Sara
Canhoto, Paulo
Salgado, Rui
Costa, Maria João
Keywords: Solar radiation
Solar energy
Solar radiation forecast
ECMWF model
Artificial neural network
Issue Date: 17-Jun-2018
Publisher: Grand Renewable Energy 2018 - International Conference and Exhibition
Citation: Sara Pereira, Paulo Canhoto, Rui Salgado, Maria João Costa, Study and development of a solar radiation predicting algorithm based on ECMWF's forecasts and ANNs. Grand Renewable Energy 2018 - International Conference and Exhibition, 17 - 22 June, 2018, Pacifico Yokohama, Japan
Abstract: This paper presents a study on the influence of Sun-Earth geometry and atmospheric variables on the predictions of solar global irradiation (GHI) obtained from the ECMWF model. It was found that the differences between predictions and measurements of GHI are correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative humidity and total water column. An artificial neural network is developed to improve predictions of GHI for four locations being the base for a predicting algorithm that can be used in energy management models of solar systems thus allowing a better management of renewable energy conversion.
URI: http://hdl.handle.net/10174/24098
Type: lecture
Appears in Collections:CGE - Comunicações - Em Congressos Científicos Internacionais

Files in This Item:

File Description SizeFormat
Apresentação_GRE2018_pdf.pdf2.36 MBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois