Evaluation and prediction of solar radiation for energy management based on neural networks; Journal of Physics: Conference Series; Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017)
| Parent link: | Journal of Physics: Conference Series Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017).— 2017.— [012036, 11 p.] |
|---|---|
| 主要作者: | |
| 企业作者: | |
| 其他作者: | |
| 总结: | Title screen Currently, 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. Режим доступа: по договору с организацией-держателем ресурса |
| 语言: | 英语 |
| 出版: |
2017
|
| 主题: | |
| 在线阅读: | http://dx.doi.org/10.1088/1742-6596/881/1/012036 http://earchive.tpu.ru/handle/11683/43867 |
| 格式: | 电子 本书章节 |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=656312 |
MARC
| LEADER | 00000nla2a2200000 4500 | ||
|---|---|---|---|
| 001 | 656312 | ||
| 005 | 20231101134920.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\22753 | ||
| 035 | |a RU\TPU\network\22740 | ||
| 090 | |a 656312 | ||
| 100 | |a 20171108a2017 k y0engy50 ba | ||
| 101 | 0 | |a eng | |
| 105 | |a y z 100zy | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Evaluation and prediction of solar radiation for energy management based on neural networks |f O. V. Aldoshina, Dinh Van Tai | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 10 tit.] | ||
| 330 | |a Currently, 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 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | 0 | |0 (RuTPU)RU\TPU\network\3526 |t Journal of Physics: Conference Series | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\22639 |t Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017) |o International Conference, 27–30 June 2017, Novosibirsk, Russian Federation |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) |v [012036, 11 p.] |d 2017 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a прогнозирование | |
| 610 | 1 | |a солнечная радиация | |
| 610 | 1 | |a управление | |
| 610 | 1 | |a энергия | |
| 610 | 1 | |a нейронные сети | |
| 610 | 1 | |a возобновляемые источники энергии | |
| 610 | 1 | |a интеллектуальные сети | |
| 610 | 1 | |a метеорологический мониторинг | |
| 610 | 1 | |a энергетические системы | |
| 610 | 1 | |a электрические нагрузки | |
| 700 | 1 | |a Aldoshina |b O. V. | |
| 701 | 0 | |a Dinh Van Tai | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет (ТПУ) |c (2009- ) |2 stltpush |3 (RuTPU)RU\TPU\col\15902 |
| 801 | 2 | |a RU |b 63413507 |c 20171109 |g RCR | |
| 856 | 4 | |u http://dx.doi.org/10.1088/1742-6596/881/1/012036 | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/43867 | |
| 942 | |c CF | ||