Abstract
This paper proposes a new approach to estimate Global Solar Radiation based on the use of the Extreme Learning Machine (ELM) technique combined with satellite data and a clear-sky model. Our study area is the radiometric station of Toledo, Spain. In order to train the Neural Network proposed, one complete year of hourly global solar radiation data (from the 1st of May 2013 to the 30th of April 2014) is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the ELM we have selected as a reference a physical-based method which considers the relation between a clear-sky index and a cloud cover index. Then a measure of the Root Mean Square Error (RMSE) and the Pearson’s Correlation Coefficient (\(r^2\)) is obtained to evaluate the performance of the suggested methodology against the reference model. We show the improvement of the results obtained by the ELM with respect to those obtained by the physical-based method considered.
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This work has been partially supported by the Spanish Ministry of Economy, through project number TIN2017-85887-C2-2-P.
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Cornejo-Bueno, L., Casanova-Mateo, C., Sanz-Justo, J., Salcedo-Sanz, S. (2018). Merging ELMs with Satellite Data and Clear-Sky Models for Effective Solar Radiation Estimation. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_19
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