2026-09-09T15:01:49Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/97122024-11-22T13:42:12Zcom_10259_4393com_10259_5086com_10259_2604col_10259_9655

00925njm 22002777a 4500 dc Granados López, Diego author García Rodríguez, Sol author García Rodríguez, Ana author Diez Mediavilla, Montserrat author Alonso Tristán, Cristina author 2022 This study evaluated the performance of nine ANNs to estimate the electricity production of a BIPV system from meteorological data that are generally accessible from ground-based meteorological stations. Solar irradiance was proved to be the most adequate input variable to predict PV production with a simple ANN. However, the accuracy of this simple model was notably improved with the inclusion of the temperature and relative humidity. Wind speed and direction were less relevant as their statistical indicators highlighted. Indeed, all reviewed ANN structures shown good performance as the ܴܯܵܧ and ܧܤܯ kept relatively low. Besides, the largest number of neurons does not lead to a better performance. 978-84-09-42477-1 http://hdl.handle.net/10259/9712 Machine learning ANN BIPV Renewable Energy Solar energy Machine learning techniques for estimation of BIPV production