Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution

An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unkno...

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Bibliographic Details
Published in:	Data science and engineering Vol. 9; no. 2; pp. 113 - 132
Main Authors:	Sun, Chenchen, Ning, Yan, Shen, Derong, Nie, Tiezheng
Format:	Journal Article
Language:	English
Published:	Singapore Springer Nature Singapore 01.06.2024 Springer Nature B.V SpringerOpen
Subjects:	Algorithm Analysis and Problem Complexity Artificial Intelligence Artificial neural networks Chemistry and Earth Sciences Computer Science Convolution Data Mining and Knowledge Discovery Database Management Dilated 1D-CNN Economic development Forecasting Graph neural networks Graph structure learning Machine learning Modules Neural networks Physics Research Paper Short-term load forecasting Spatial dependencies Statistics for Engineering Systems and Data Security Temporal dependencies Graph structure learning Graph neural networks Short-term load forecasting Dilated 1D-CNN Temporal dependencies Spatial dependencies
ISSN:	2364-1185, 2364-1541
Online Access:	Get full text
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