Enhancing predictive accuracy in environmental data analysis: a hybrid LASSO-RFR approach for climatic analysis in Siberia
| Parent link: | Перспективы развития фундаментальных наук=Prospects of Fundamental Sciences Development: сборник научных трудов XXI Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 23-26 апреля 2024 г./ Национальный исследовательский Томский политехнический университет ; под ред. И. А. Курзиной [и др.].— .— Томск: Изд-во ТПУ Т. 3 : Математика.— 2024.— С. 23-25 |
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| Další autoři: | , , |
| Shrnutí: | Заглавие с экрана This study introduces a hybrid LASSO-RFR approach for photovoltaic energy forecasting, leveraging LASSO's feature selection with RFR's analytical strength to tackle weather-induced variability. It showcases improved forecast accuracy through simplified datasets and enhanced correlation analysis, resulting in superior model performance. With an MSE of 0.0060 and an R squared of 85.7% for Model 2, the approach outperforms LASSO-only models, marking a significant advancement in renewable energy analytics and offering a potent forecasting tool for areas with extreme weather. Текстовый файл |
| Jazyk: | ruština |
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2024
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| On-line přístup: | https://earchive.tpu.ru/handle/11683/80585 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674458 |
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| 200 | 1 | |a Enhancing predictive accuracy in environmental data analysis: a hybrid LASSO-RFR approach for climatic analysis in Siberia |f D. A. Akpuluma, J. I. Abam, C. A. Williams |g Scientific Supervisor A. V. Yurchenko ; Tomsk Polytechnic University | |
| 300 | |a Заглавие с экрана | ||
| 320 | |a Список литературы: 9 назв. | ||
| 330 | |a This study introduces a hybrid LASSO-RFR approach for photovoltaic energy forecasting, leveraging LASSO's feature selection with RFR's analytical strength to tackle weather-induced variability. It showcases improved forecast accuracy through simplified datasets and enhanced correlation analysis, resulting in superior model performance. With an MSE of 0.0060 and an R squared of 85.7% for Model 2, the approach outperforms LASSO-only models, marking a significant advancement in renewable energy analytics and offering a potent forecasting tool for areas with extreme weather. | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |0 674010 |t Перспективы развития фундаментальных наук |l Prospects of Fundamental Sciences Development |o сборник научных трудов XXI Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 23-26 апреля 2024 г. |9 674010 |c Томск |n Изд-во ТПУ |f Национальный исследовательский Томский политехнический университет ; под ред. И. А. Курзиной [и др.] | |
| 463 | 1 | |0 674240 |t Т. 3 : Математика |v С. 23-25 |d 2024 |9 674240 |p 1 файл (17,1 MB, 96 с.) |u conference_tpu-2024-C21_V3.pdf |l Vol. 3 : Mathematics | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a climate data analysis | |
| 610 | 1 | |a statistical modelling | |
| 610 | 1 | |a hybrid model | |
| 700 | 1 | |a Akpuluma |b D. A. | |
| 701 | 1 | |a Abam |b J. I. | |
| 701 | 1 | |a Williams |b C. A. | |
| 702 | 1 | |a Yurchenko |b A. V. |c physicist |c Professor of Tomsk Polytechnic University, Doctor of Technical Sciences |f 1974- |g Aleksey Vasilievich | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |c (2009- ) |9 26305 |4 570 |
| 801 | 0 | |a RU |b 63413507 |c 20240910 |g RCR | |
| 856 | 4 | |z https://earchive.tpu.ru/handle/11683/80585 |u https://earchive.tpu.ru/handle/11683/80585 | |
| 942 | |c CF | ||