Forecasting very short-term power load with hybrid interpretable deep models

Forecasting very short-term power load with hybrid interpretable deep models

Very Short-Term power Load Forecasting (VSTLF) plays a key role in electricity planning and operational transactions. However, with the growing complexity and uncertainty of today’s electricity requirements, e.g. nonlinear correlations across electricity factors, unpredictable wavy trends and sudden...

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Bibliographic Details
Main Authors:	Zhihe Yang, Jiandun Li, Chang Liu, Haitao Wang
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-12-01
Series:	Systems Science & Control Engineering
Subjects:	Very short-term power load forecasting convolutional neural network bidirectional long short-term memory interpretability shapley additive explanations
Online Access:	https://www.tandfonline.com/doi/10.1080/21642583.2025.2486136
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