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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Data science and engineering Jg. 9; H. 2; S. 113 - 132
Hauptverfasser: Sun, Chenchen, Ning, Yan, Shen, Derong, Nie, Tiezheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.06.2024
Springer Nature B.V
SpringerOpen
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-023-00233-8