Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5888
Título: Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines
Autor: Marques, Luis
Pinto, Tiago
Sousa, Tiago
Praça, Isabel
Vale, Zita
Abreu, Samuel L.
Palavras-chave: Artificial Neural Networks
Data Mining
Machine Learning
Solar forecasting
Support Vector Machines
Data: 28-Out-2014
Editora: ELECON Project
Relatório da Série N.º: ELECON;2014
Resumo: This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.
URI: http://hdl.handle.net/10400.22/5888
Versão do Editor: http://www.elecon.ipp.pt/images/Workshop2/Proceedings_elecon_2014.pdf
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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