Predictive clarity in energy analytics: xai-enhanced solar forecasting in Siberia; Молодежь и современные информационные технологии

Dettagli Bibliografici
Parent link:	Молодежь и современные информационные технологии.— 2024.— С. 230-234
Altri autori:	Akpuluma D. A., Yurchenko A. V. Aleksey Vasilievich, Abam J. I., Williams C. A.
Riassunto:	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
Lingua:	inglese
Pubblicazione:	2024
Serie:	Искусственный интеллект, машинное обучение и большие данные
Soggetti:	электронный ресурс труды учёных ТПУ climate data analysis hybrid models XAI implementation
Accesso online:	http://earchive.tpu.ru/handle/11683/84827
Natura:	Elettronico Capitolo di libro
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675441

Descrizione
Riassunto:	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

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