2026-11-01T22:00:08Zhttps://riubu.ubu.es/oai/request

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

Granados López, Diego García Rodríguez, Sol García Rodríguez, Ana Diez Mediavilla, Montserrat Alonso Tristán, Cristina 2024-11-19T11:27:36Z 2024-11-19T11:27:36Z 2022 978-84-09-42477-1 http://hdl.handle.net/10259/9712 Comunicación presentada en: XII Congreso Nacional y III Internacional de Ingeniería Termodinámica (12 CNIT), June 19- July 1, Madrid (Spain) 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. Financial support provided by the Spanish Ministry of Science & Innovation (Ref. RTI2018- 098900-B-I00) and PIRTU Program, ORDEN EDU/556/2019, for financial support. application/pdf eng Proceedings 12CNIT 2022, p. 63-68 Machine learning ANN BIPV Renewable Energy Solar energy Termodinámica Energía solar Thermodynamics Solar energy Machine learning techniques for estimation of BIPV production info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/openAccess