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abstract only active powe artificial intelligence automotive fuels autonomous energy systems available transfer capability chronological method. clustering algorithms comparative effectiveness competitiveness computer simulation computer software decision making decision supports deep learning deep learning. demand distributed generation dynamic state estimation eastern regions economic and social effects economic development economic variables economics economy elasticity electric automobiles electric car electric cars electric lines electric power generation electric power system control electric power systems electric power transmission networks electric power utilization electric utilities electricity intensities electricity-consumption energy conservation energy consumption energy demand energy demands energy development program energy infrastructure development forecasting energy infrastructures energy intensity energy management energy resources energy sector energy storage energy utilization estimation program expert decision support expert knowledge forecast forecasting forecasting modeling forecasting models forecasting problems freight transportation fuel and energy sector fuzzy c-means (fcm) fuzzy systems gru households hybrid approach integral equations intelligent computing intelligent info intersystem inverse problem isolated hybrid power systems knowledge management learning algorithms least squares approximations long-term forecasting long-term tre longterm trends lstm machine learning macro-region manufacture measurements model modeling models motor fuel neural netwo neural networks numerical methods on-line controls ontology operation constraint optimal resu per capita per capita energy consumption perspective pollution power plants power systems power systems mathematical modeling and control prices real time recursive least squares method (rls) regional markets regions of russia renewable energy sources retrospective analysis risks road transport roads and streets service industri service industry signal processing solar radiation state estimation state estimators state variables support vector machines svm t-s fuzzy model total transfer c transport uncertainty wind wind effects wind energy potential wind power wind power forecasting wind ramp prediction инвестиционные риски моделирование неопределенность прогнозирование риски сравнительная эффективность экономика электростанции энергетика

   
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Mathematical modelling of failures of electrical grid (10 kv) of autonomous energy systems with renewable distributed generation

Karamov D.N., Naumov I.V., Perzhabinsky S.M. Mathematical modelling of failures of electrical grid (10 kv) of autonomous energy systems with renewable distributed generation // Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering. Vol.329. №7. 2018. P.116-130. https://elibrary.ru/download/elibrary_35410988_83631417.pdf Relevance of the work is caused by the improvement of the existing approaches to modeling possible component failures of autonomous power systems with distributed...

Теги: autonomous energy systems , chronological method. , distributed generation , forecasting , renewable energy sources , solar radiation , wind energy potential
Раздел: ИСЭМ СО РАН
The impact of economic parameter uncertainty growth on regional energy demand assessment

Mazurova O.V., Galperova E.V. The impact of economic parameter uncertainty growth on regional energy demand assessment // Economy of Region. Vol.13. No.2. 2017. P.465-476. DOI: 10.17059/2017-2-12 The article deals with the forecasting studies based on the energy demand and prices in the region in terms of the complex interconnections between economy (and energy) and the growth of uncertainty of the future development of the country and territories. The authors propose a ...

Теги: forecasting , uncertainty , energy resources , prices , competitiveness , regions of russia , regional markets , demand , fuel and energy sector , elasticity , macro-region , model
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 and ...

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
Forecasting of available transfer capability in intersystem transmission lines

Glazunova A.M., Aksaeva E.S., Semshchikov E.S. Forecasting of available transfer capability in intersystem transmission lines // 2016 IEEE International Energy Conference, ENERGYCON 2016. 1 p. ISBN (print): 9 781 467 . DOI: 10.1109/ENERGYCON.2016.7514127 The paper develops a technique of available ...

Теги: electric power systems , forecasting , measurements , neural networks , available transfer capability , dynamic state estimation , intersystem , real time , state estimators , state variables , total transfer c
Раздел: ИСЭМ СО РАН
Forecasting the Total Transfer Capability of Intersystem Lines for On-line Control of Electric Power System Operation

Glazunova A.M., Aksaeva E.S., Semshchikov E.S. Forecasting the Total Transfer Capability of Intersystem Lines for On-line Control of Electric Power System Operation // IFAC-PapersOnLine. Vol.49. No.27. 2016. P.455-460. DOI: 10.1016/j.ifacol.2016.10.775 The paper develops a technique of total transfer ...

Теги: electric lines , electric power systems , forecasting , measurements , neural networks , state estimation , dynamic state estimation , estimation program , on-line controls , operation constraint , optimal resu
Раздел: ИСЭМ СО РАН
Способы повышения эффективности долгосрочных прогнозов ТЭК

Кононов Ю.Д., Кононов Д.Ю. Способы повышения эффективности долгосрочных прогнозов ТЭК // Энергетическая политика. №1. 2013. C.53-60.

Теги: прогнозирование , энергетика , экономика , неопределенность , риски , моделирование , forecasting , energy sector , economy , uncertainty , risks , modeling
Раздел: ИСЭМ СО РАН


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