@misc{10481/78405,
year = {2022},
month = {11},
url = {https://hdl.handle.net/10481/78405},
abstract = {Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic
panel efficiency along with a downward trend in production costs. In addition, the European Union
is committed to easing the implementation of renewable energy in many companies in order to
obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and
uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic
systems. This research addresses this problem by implementing intelligent models to predict the
production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally,
the models were able to accurately predict the energy to be produced in the next hour using historical
information as predictor variables.},
organization = {Ministry of Science and Innovation, Spain (MICINN)
Spanish Government	PID2020-112495RB-C21},
organization = {I + D + i FEDER	B-TIC-42-UGR20},
publisher = {MDPI},
keywords = {Photovoltaic energy},
keywords = {Machine learning},
keywords = {Energy forecasting},
keywords = {Solar farm},
title = {Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study},
doi = {10.3390/en15228732},
author = {Cabezón, L. and Baca Ruiz, Luis Gonzaga and Criado Ramón, David and Jadraque Gago, Eulalia and Pegalajar Jiménez, María Del Carmen},
}