Deep neural networks for the quantile estimation of regional renewable energy production
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s10489-022-03958-7 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7391 |
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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 |
| _version_ |
1761912526981824512 |
| score |
8.887836 |