Voropai N., Rehtanz C., Kippelt S., Tomin N., Haeger U., Efimov D., Kurbatsky V., Kolosok I. The development of a joint modelling framework for operational flexibility in power systems // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771685. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771685 The TU Dortmund University (Germany) and the Energy Systems Institute of the Russian Academy of Sciences (Russia) launched a joint research...
Теги: artificial intelligence , electric power system , flexibility , machine learning , power system security , electric machinery , electric power systems , learning systems , different layers , energy systems , modelling framework , operational flexibility , russian aca... Optimization Methods in ESI RAS / SEI Ac. Sc. USSR (survey) More talks TBA Apart from Plenary/Section Sessions, the programm will include the Technical Tour to the Corporate Educational and Research Center of JSC "Irkutskenego", round table on Machine Learning & AI, NSFC Project Meeting and International science and technology cooperation program Project Meeting. The scientific tour to the Limnology Museum of RAS (Listvyanka, lake Baikal) is scheduled.
Теги: power systems mathematical modeling and control , forecasting , isolated hybrid power systems , wind ramp prediction , machine learning... bear, so special attentions are paid to the application of AI techniques in reactive voltage control and a lot of results in this field are obtained by many authors. This paper presents a hybrid Volt/VAr control approach based on AI techniques such as machine learning and multi-agent systems based models. Proposed approach enjoys high efficiency for various scenarios of modified IEEE 6-Bus and 118-Bus test systems. © 2018 входит
Теги: machine learning , multi-agent system , power system , random forest , security , volt-var control , decision trees , intelligent agents , learning systems , value engineering , voltage regulators , inherent characteristics , optimal solutions , random forests , traditБояркин Д.А., Крупенев Д.С., Якубовский Д.В. Использование методов машинного обучения при оценке надёжности электроэнергетических систем методом Монте-Карло // Вестник ЮУрГУ. Серия «Математическое моделирование и программирование». Т.11. №4. 2018. C.146-153. DOI: 10/14529/mmp18041 В статье рассматривается вопрос повышения вычислительной эффективности процедуры оценки балансовой надежности электроэнергетических систем при использовании метода статистических испытаний (метод Монте-Карло). При использовании...
Теги: электроэнергетические системы , оценка надежности , метод монте-карло , машинное обучение , electric power systems , adequacy assessment , monte carlo method , machine learning... dynamics of ionospheric parameters is an actual and at the same time rather complicated task. One of the main issues is the selection of control parameters for constructing accurate predictive model (feature selection). The approach is based on the machine learning technology for this problem solution. The vertical absolute total electron content (TEC) with a time resolution of 30 minutes is used as experimental data. The data were obtained using phase and group measurements of TEC at the mid-latitude ...
Теги: absolute total electron content , gradient boosting , machine learning , nowcasting , random forest , support vector machine... Russian Academy of Sciences from July 31 to August 6, 2017. This special issue contains some extended talks of the school-seminar, highlighting theoretical and applied results aimed to show the use of the state-of-the-art operations research methods and machine learning technologies in various applications. © 2018 [International Journal of Artificial Intelligence]. нет
Теги: editorial , machine learning , operations research , optimizationBoyarkin D.., Krupenev D.S., Iakubovskiy D.., Sidorov D.N. Machine learning in electric power systems adequacy assessment using Monte-Carlo method // Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. ID: 8109871. P.201-205. ISBN (print): 9781538615966....
Теги: adequacy assessment , electric power systems , machine learning , monte carlo method , random forest , support vector machine , artificial intelligence , computational efficiency , decision trees , efficiency , learning systems , problem solving , support vector machZhukov A.V., Sidorov D.N., Foley A.M. Random forest based approach for concept drift handling // Communications in Computer and Information Science. Vol.661. 2017. P.69-77. ISBN (print): 978-3-319-52920-2; 978-3-319-52919-6. DOI: 10.1007/978-3-319-52920-2_7 Concept drift has potential in smart grid analysis because the socio-economic behaviour of consumers is not governed by the laws of physics. Likewise there are also applications in wind power forecasting. In this paper we present decision tree...
Теги: machine learning , decision tree , concept drift , ensemble learning , classification , random forest , classification (of information) , decision trees , image analysis , learning algorithms , learning systems , wind power , aggregation rules , concept drifts , empiri... Russian Academy of Sciences from June 30 July 6 in 2014. In this special issue, we have invited the contributors to this event to expand on their presentations and highlight theoretical results in the field and to demonstrate the use of optimisation and machine learning methods in variour applications. нет
Теги: editorial , optimisation , machine learning
