RT Journal Article
T1 Deep neural networks for the quantile estimation of regional renewable energy production
A1 Alcántara Mata, Antonio
A1 Galván, Inés M.
A1 Aler, Ricardo
AB Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.
PB Springer
SN 0924-669X
YR 2022
FD 2022-08-02
LK https://hdl.handle.net/10016/35678
UL https://hdl.handle.net/10016/35678
LA eng
NO This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN /AEI / 10.13039 / 501100011033. This work was also supported by the Comunidad de Madrid Excellence Program.
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS e-Archivo
RD 17 jul. 2024