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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
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
Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution

... and statistics cannot guarantee the accuracy of multivariable dynamic prediction under the background of high randomness and big data. In recent years, with the improvement of hardware computing ability and the large-scale increase of training data, deep learning has been widely applied in the field of forecasting. This paper focuses on the analysis of the application of recurrent neural networks (RNN), an advanced algorithm in deep learning, in the forecasting task. The forecasting models based ...

Теги: deep learning , forecasting , gru , lstm , decision making , intelligent computing , learning algorithms , machine learning , pollution , forecasting modeling , forecasting models , forecasting problems , neural netwo
Раздел: ИСЭМ СО РАН
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


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