Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network

Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network

The burgeoning proliferation of integrated energy systems has fostered an unprecedented degree of coupling among various energy streams, thereby elevating the necessity for unified multi-energy forecasting (MEF). Prior approaches predominantly relied on independent predictions for heterogeneous load...

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Bibliographic Details
Main Authors: Han Guo, Bin Huang, Jianhui Wang
Format: Article
Language:English
Published: Elsevier 2024-07-01
Series:Advances in Applied Energy
Subjects:
Attention mechanism
Load forecasting
Multi-task learning
Probabilistic forecasting
Temporal convolutional network
Online Access:http://www.sciencedirect.com/science/article/pii/S2666792424000039

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http://www.sciencedirect.com/science/article/pii/S2666792424000039

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