RT info:eu-repo/semantics/conferenceObject
T1 Machine learning techniques for estimation of BIPV production
A1 Granados López, Diego
A1 García Rodríguez, Sol
A1 García Rodríguez, Ana
A1 Diez Mediavilla, Montserrat
A1 Alonso Tristán, Cristina
K1 Machine learning
K1 ANN
K1 BIPV
K1 Renewable Energy
K1 Solar energy
K1 Termodinámica
K1 Thermodynamics
K1 Energía solar
K1 Solar energy
AB 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.
SN 978-84-09-42477-1
YR 2022
FD 2022
LK http://hdl.handle.net/10259/9712
UL http://hdl.handle.net/10259/9712
LA eng
NO Comunicación presentada en: XII Congreso Nacional y III Internacional de Ingeniería Termodinámica (12 CNIT), June 19- July 1, Madrid (Spain)
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024