On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods

Athanassios Zagouras, Hugo T.C. Pedro and Carlos F.M. Coimbra

Renewable Energy, 2015, vol. 78, issue C, 203-218

Abstract: We propose and analyze a spatio–temporal correlation method to improve forecast performance of solar irradiance using gridded satellite-derived global horizontal irradiance (GHI) data. Forecast models are developed for seven locations in California to predict 1-h averaged GHI 1, 2 and 3 h ahead of time. The seven locations were chosen to represent a diverse set of maritime, mediterranean, arid and semi-arid micro-climates. Ground stations from the California Irrigation Management Information System were used to obtain solar irradiance time-series from the points of interest. In this method, firstly, we define areas with the highest correlated time-series between the satellite-derived data and the ground data. Secondly, we select satellite-derived data from these regions as exogenous variables to several forecast models (linear models, Artificial Neural Networks, Support Vector Regression) to predict GHI at the seven locations. The results show that using linear forecasting models and a genetic algorithm to optimize the selection of multiple time-lagged exogenous variables results in significant forecasting improvements over other benchmark models.

Keywords: Solar forecasting; Time-lagged correlation; Gridded data; Exogenous input data; Genetic algorithms (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:78:y:2015:i:c:p:203-218

DOI: 10.1016/j.renene.2014.12.071

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