Бояркин 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 learningBoyarkin D.A., Krupenev D.S., Iakubovskii D.V. Machine learning in electric power systems adequacy assessment using Monte-Carlo method // Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software. Vol.11. No.4. 2018. P.146-153. DOI: 10.14529/mmp180411 ...
Теги: adequacy assessment , electric power systems , machine learning , monte carlo method... and Prevention of Blackouts in Power Interconnections // Automation and Remote Control. Vol.79. No.10. 2018. P.1741-1755. DOI: 10.1134/S0005117918100016 We propose a suite of intelligent tools based on the integration of methods of agent modeling and machine learning for the improvement of protection systems and emergency automatics. We propose an online approach to the assessment and management of dynamic security of electric power systems (EPS) with the use of a streaming modification of the random ...
Теги: agent modeling , electric power systems , emergency automatics , l-index , machine learning , voltage collapse , artificial intelligence , decision trees , electric power system interconnection , learning systems , online systems , software agents , agent model , elec... 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
