Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

This study evaluates and differentiates five advanced machine learning models-LSTM, GRU, CNN-LSTM, Random Forest, and SVR-aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting. LSTM achieved a remarkable Mean Squared Error (MSE) of 0.010 and R2 score o...

書誌詳細
出版年:	DISCOVER SUSTAINABILITY
主要な著者:	Khan, Sunawar; Mazhar, Tehseen; Khan, Muhammad Amir; Shahzad, Tariq; Ahmad, Wasim; Bibi, Afsha; Saeed, Mamoon M.; Hamam, Habib
フォーマット:	論文
言語:	English
出版事項:	SPRINGERNATURE 2024
主題:	Science & Technology - Other Topics; Environmental Sciences & Ecology
オンライン･アクセス:	https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386434000001

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