Predictive clarity in energy analytics: xai-enhanced solar forecasting in Siberia

Predictive clarity in energy analytics: xai-enhanced solar forecasting in Siberia

書目詳細資料
Parent link:	Молодежь и современные информационные технологии: сборник трудов XXI Международной научно-практической конференции студентов, аспирантов и молодых ученых, 15–18 апреля 2024 г., Томск/ ред. кол. А. С. Фадеев, Н. Г. Марков, В. Г. Спицын [и др.]. С. 230-234.— .— Томск: Изд-во ТПУ, 2024.— conference_tpu-2024-C04.pdf
其他作者:	Akpuluma D. A., Yurchenko A. V. Aleksey Vasilievich, Abam J. I., Williams C. A.
總結:	This study unveils a robust LASSO-RFR hybrid model for solar power prediction in Siberia, significantly enhancing predictive accuracy and reducing MSE, with an R-squared of 85.9 %. Employing LIME and SHAP for XAI, it foregrounds feature contributions, fostering transparent, reliable forecasting in extreme climates Текстовый файл
語言:	英语
出版:	2024
叢編:	Искусственный интеллект, машинное обучение и большие данные
主題:	электронный ресурс труды учёных ТПУ climate data analysis hybrid models XAI implementation
在線閱讀:	http://earchive.tpu.ru/handle/11683/84827
格式:	電子 Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675441

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