The Time series during training analysis: (a) hourly data, (b) daily data, (c) weekly data.

<p>The Time series during training analysis: (a) hourly data, (b) daily data, (c) weekly data.</p>

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Bibliographic Details
Main Author:	Abrar Ahmed (786961) (author)
Other Authors:	Safdar Ali (3095124) (author), Ali Raza (3558965) (author), Ibrar Hussain (2365528) (author), Ahmad Bilal (13327050) (author), Norma Latif Fitriyani (20521497) (author), Yeonghyeon Gu (20521500) (author), Muhammad Syafrudin (20521503) (author)
Published:	2025
Subjects:	Physiology Evolutionary Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified mean absolute error make informed decisions experimental evaluations expressed capturing complex temporal xlink "> energy forecasting energy needs energy prediction focus enhanced accuracy measures cnns discover patterns energy generation resources energy generation companies proposed model outperformed proposed hybrid model forecasting energy consumption term energy consumption lstm identifies long simultaneously predict short energy generation proposed model energy consumption term accuracy sequential patterns simultaneously predict predict short traditional models term memory term dependencies superior performance spatial features short term research proposes rapid increase often failing model capable lstm ), long term existing state every country either short economic development directional lstm development leads costs associated become crucial art models
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