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Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

Liu F., Li R., Dreglea A. Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model // Energies. Vol.12. No.18. ID: 3551. 2019. DOI: 10.3390/en12183551 Accurate wind power and wind speed forecasting remains a critical challenge in wind power ...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
Раздел: ИСЭМ СО РАН
Intelligent control of a wind turbine based on reinforcement learning

Tomin N., Kurbatsky V., Guliyev H. Intelligent control of a wind turbine based on reinforcement learning // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771645. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771645 Advanced controllers of modern ...

Теги: control , mimo control , pitch control , reinforcement learning , torque control , wind turbine , adaptive control systems , control engineering , electric machinery , machine learning , mimo systems , stochastic systems , wind , wind turbines , adaptive control des
Раздел: ИСЭМ СО РАН
Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

... information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate ...

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
Dynamic State Estimation of Electric Power System Integrating Wind Power Generation

Glazunova A. Dynamic State Estimation of Electric Power System Integrating Wind Power Generation // E3S Web of Conferences. Vol.69. ID: 02013. 2018. DOI: 10.1051/e3sconf/20186902013 This paper is concerned with a problem of operation control of the electric power systems integrating wind farms. Dynamic state estimation is used ...

Теги: electric power generation , electric power system control , electric power systems , electric power transmission networks , electric utilities , forecasting , state estimation , wind , wind power , active powe
Раздел: ИСЭМ СО РАН
Adequacy analysis of electric power systems with wind and solar power stations

Karamov D., Perzhabinsky S. Adequacy analysis of electric power systems with wind and solar power stations // E3S Web of Conferences. Vol.58. ID: 02019. 2018. DOI: 10.1051/e3sconf/20185802019 We developed a new method of adequacy analysis of electric power systems with wind and solar power stations. There are storage batteries ...

Теги: data handling , digital storage , electric power supplies to apparatus , electric power systems , meteorology , monte carlo methods , secondary batteries , solar energy , solar radiation , wind , air storage , e
Раздел: ИСЭМ СО РАН
Short-term wind power forecasting based on T-S fuzzy model

Liu F., Li R., Li Y. et al. Short-term wind power forecasting based on T-S fuzzy model // Asia-Pacific Power and Energy Engineering Conference, APPEEC. Vol.Decem. 2016. P.414-418. ISBN 9781509054183. DOI: 10.1109/APPEEC.2016.7779537. Due to the impacts of wind speed, wind direction, temperature ...

Теги: fuzzy c-means (fcm) , recursive least squares method (rls) , t-s fuzzy model , wind power forecasting , clustering algorithms , forecasting , fuzzy systems , least squares approximations , signal processing , support vector machines , wind , wind effects , wind power
Раздел: ИСЭМ СО РАН


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