Enhancing predictive accuracy in environmental data analysis: a hybrid LASSO-RFR approach for climatic analysis in Siberia; Перспективы развития фундаментальных наук; Т. 3 : Математика

Enhancing predictive accuracy in environmental data analysis: a hybrid LASSO-RFR approach for climatic analysis in Siberia; Перспективы развития фундаментальных наук; Т. 3 : Математика

Bibliographic Details
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
Main Author: Akpuluma D. A.
Corporate Author: Томский политехнический университет (570)
Other Authors: Abam J. I., Williams C. A., Yurchenko A. V. Aleksey Vasilievich
Summary: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
Language:Russian
Published: 2024
Subjects:
труды учёных ТПУ
электронный ресурс
climate data analysis
statistical modelling
hybrid model
Online Access:https://earchive.tpu.ru/handle/11683/80585
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674458

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