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pro vyhledávání: '"Arbod, Guillaume"'
Autor:
Nie, Yuhao, Paletta, Quentin, Scott, Andea, Pomares, Luis Martin, Arbod, Guillaume, Sgouridis, Sgouris, Lasenby, Joan, Brandt, Adam
Solar forecasting from ground-based sky images has shown great promise in reducing the uncertainty in solar power generation. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-b
Externí odkaz:
http://arxiv.org/abs/2211.02108
Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt
Externí odkaz:
http://arxiv.org/abs/2206.03207
Autor:
Nie, Yuhao, Paletta, Quentin, Scott, Andea, Pomares, Luis Martin, Arbod, Guillaume, Sgouridis, Sgouris, Lasenby, Joan, Brandt, Adam
Publikováno v:
In Applied Energy 1 September 2024 369
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional archit
Externí odkaz:
http://arxiv.org/abs/2111.14507
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on
Externí odkaz:
http://arxiv.org/abs/2104.12419
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels. Ho
Externí odkaz:
http://arxiv.org/abs/2102.00721
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Publikováno v:
In Applied Energy 15 April 2023 336
Publikováno v:
In Applied Energy 15 November 2022 326