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1d convolutional neural networks aerodynamic diameters air pollutant air pollution air pollution control air pollution forecasting application scenario artificial intelligence bidirectional gated recurrent unit convolution deep learning deep neural networks electric power generation electric power transmission networks electrical energy systems energy prediction energy storage forecasting future research directions hybrid ac/dc power system integral equations lakes machine learning nonlinear optimiz photovoltaic power generation recurrent neural networks renewable energy source solar energy solar power forecast stochastic optimization stochastic systems taxonomies taxonomy volterra models wind

   

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Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

Tao Q., Liu F., Li Y., Sidorov D. Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU // IEEE Access. Vol.7. ID: 8732985. 2019. P.76690-76698. DOI: 10.1109/ACCESS.2019.2921578 Air pollution forecasting can provide reliable information about the future pollution situation, ...

Теги: 1d convolutional neural networks , air pollution forecasting , bidirectional gated recurrent unit , deep learning , air pollution , air pollution control , convolution , deep neural networks , recurrent neural networks , wind , aerodynamic diameters , air pollutant
Раздел: ИСЭМ СО РАН
Toward zero-emission hybrid AC/DC power systems with renewable energy sources and storages: A case study from Lake Baikal region

Sidorov D., Panasetsky D., Tomin N., Karamov D., Zhukov A., Muftahov I., Dreglea A., Liu F., Li Y. Toward zero-emission hybrid AC/DC power systems with renewable energy sources and storages: A case study from Lake Baikal region // Energies. Vol.13. No.5. ID: 1226. 2020. DOI: 10.3390/en13051226 Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting...

Теги: forecasting , hybrid ac/dc power system , machine learning , renewable energy source , stochastic optimization , volterra models , deep learning , electric power generation , electric power transmission networks , energy storage , integral equations , lakes , optimiz
Раздел: ИСЭМ СО РАН
Taxonomy research of artificial intelligence for deterministic solar power forecasting

... secure operation of electrical energy systems. It is therefore imperative to improve the prediction accuracy of solar power to prepare for the unknown conditions in the future. So far, artificial intelligence (AI) algorithms such as machine learning and deep learning have been widely-reported with competitive prediction performance because they can reveal the invariant structure and nonlinear features in solar data. However, these reports have not been fully reviewed. Accordingly, this paper provides ...

Теги: artificial intelligence , photovoltaic power generation , solar power forecast , taxonomy , deep learning , solar energy , stochastic systems , taxonomies , application scenario , electrical energy systems , energy prediction , future research directions , nonlinear
Раздел: ИСЭМ СО РАН


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