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<div><p>Accurate power load prediction is crucial for optimizing energy consumption and enhancing efficiency in industrial environments. However, the highly nonlinear and non-stationary nature of power load time series presents significant challenges. To address this, we propose a novel...

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Main Author:	Yin Luo (160903) (author)
Other Authors:	Chaofan Guo (20595965) (author), Minfeng Pan (22056996) (author), Hong Zhou (29614) (author)
Published:	2025
Subjects:	Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified winged kite algorithm variational mode decomposition temporal pattern learning sequential pattern learning range temporal dependencies power load forecasting optimizing energy consumption model &# 8217 local feature extraction improving signal representation enhance feature extraction deep neural networks advanced sequence modeling emphasize critical features ablation study highlights critical roles ablation studies stationary nature rmse ). results demonstrate reducing noise industrial environments including comparisons highly nonlinear future research findings underscore extensive experiments enhancing generalization enhancing efficiency decomposed signals attention mechanism art models
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