Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41931
Title: Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting
Authors: Aguiar, L. Mazorra 
Pereira, B
Lauret, P.
Díaz, F. 
David, M.
UNESCO Clasification: 210601 Energía solar
3322 Tecnología energética
250121 Simulación numérica
Keywords: Artificial neural networks
Numerical weather prediction
Satellite images
Solar forecasting
Issue Date: 2016
Journal: Renewable Energy 
Abstract: Isolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts.
URI: http://hdl.handle.net/10553/41931
ISSN: 0960-1481
DOI: 10.1016/j.renene.2016.06.018
Source: Renewable Energy[ISSN 0960-1481],v. 97, p. 599-610
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