| 000 | 03300nla2a2200481 4500 | ||
|---|---|---|---|
| 001 | 656312 | ||
| 005 | 20231030041422.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\22753 | ||
| 035 | _aRU\TPU\network\22740 | ||
| 090 | _a656312 | ||
| 100 | _a20171108a2017 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 105 | _ay z 100zy | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aEvaluation and prediction of solar radiation for energy management based on neural networks _fO. V. Aldoshina, Dinh Van Tai |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: 10 tit.] | ||
| 330 | _aCurrently, there is a high rate of distribution of renewable energy sources and distributed power generation based on intelligent networks; therefore, meteorological forecasts are particularly useful for planning and managing the energy system in order to increase its overall efficiency and productivity. The application of artificial neural networks (ANN) in the field of photovoltaic energy is presented in this article. Implemented in this study, two periodically repeating dynamic ANS, that are the concentration of the time delay of a neural network (CTDNN) and the non-linear autoregression of a network with exogenous inputs of the NAEI, are used in the development of a model for estimating and daily forecasting of solar radiation. ANN show good productivity, as reliable and accurate models of daily solar radiation are obtained. This allows to successfully predict the photovoltaic output power for this installation. The potential of the proposed method for controlling the energy of the electrical network is shown using the example of the application of the NAEI network for predicting the electric load. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | 0 |
_0(RuTPU)RU\TPU\network\3526 _tJournal of Physics: Conference Series |
|
| 463 | 0 |
_0(RuTPU)RU\TPU\network\22639 _tVol. 881 : Innovations in Non-Destructive Testing (SibTest 2017) _oInternational Conference, 27–30 June 2017, Novosibirsk, Russian Federation _o[proceedings] _fNational Research Tomsk Polytechnic University (TPU) _v[012036, 11 p.] _d2017 |
|
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aпрогнозирование | |
| 610 | 1 | _aсолнечная радиация | |
| 610 | 1 | _aуправление | |
| 610 | 1 | _aэнергия | |
| 610 | 1 | _aнейронные сети | |
| 610 | 1 | _aвозобновляемые источники энергии | |
| 610 | 1 | _aинтеллектуальные сети | |
| 610 | 1 | _aметеорологический мониторинг | |
| 610 | 1 | _aэнергетические системы | |
| 610 | 1 | _aэлектрические нагрузки | |
| 700 | 1 |
_aAldoshina _bO. V. |
|
| 701 | 0 | _aDinh Van Tai | |
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет (ТПУ) _c(2009- ) _2stltpush _3(RuTPU)RU\TPU\col\15902 |
| 801 | 2 |
_aRU _b63413507 _c20171109 _gRCR |
|
| 856 | 4 | _uhttp://dx.doi.org/10.1088/1742-6596/881/1/012036 | |
| 856 | 4 | _uhttp://earchive.tpu.ru/handle/11683/43867 | |
| 942 | _cCF | ||