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Deep neural networks for the quantile estimation of regional renewable energy production

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Main Authors: Antonio, Alcántara, Inés M., Galván, Ricardo, Aler
Format: Book
Language:English
Published: Springer 2023
Subjects:
renewable energy
electrical power systems
Online Access:https://link.springer.com/article/10.1007/s10489-022-03958-7
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7391
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spelling oai:localhost:PNK-73912023-03-31T07:40:02Z Deep neural networks for the quantile estimation of regional renewable energy production Antonio, Alcántara Inés M., Galván Ricardo, Aler renewable energy electrical power systems CC BY 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. 2023-03-31T07:40:02Z 2023-03-31T07:40:02Z 2023 Book https://link.springer.com/article/10.1007/s10489-022-03958-7 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7391 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic renewable energy
electrical power systems
spellingShingle renewable energy
electrical power systems
Antonio, Alcántara
Inés M., Galván
Ricardo, Aler
Deep neural networks for the quantile estimation of regional renewable energy production
description CC BY
format Book
author Antonio, Alcántara
Inés M., Galván
Ricardo, Aler
author_facet Antonio, Alcántara
Inés M., Galván
Ricardo, Aler
author_sort Antonio, Alcántara
title Deep neural networks for the quantile estimation of regional renewable energy production
title_short Deep neural networks for the quantile estimation of regional renewable energy production
title_full Deep neural networks for the quantile estimation of regional renewable energy production
title_fullStr Deep neural networks for the quantile estimation of regional renewable energy production
title_full_unstemmed Deep neural networks for the quantile estimation of regional renewable energy production
title_sort deep neural networks for the quantile estimation of regional renewable energy production
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s10489-022-03958-7
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7391
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score 8.887836

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