.png){kind=icon}
返回
Graph Neural Network-Based Short-Term Load Forecasting with Temporal Convolution
.png){kind=icon}
OA
AI
DOI：10.1007/s41019-023-00233-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Short-term load forecasting
Graph structure learning
Graph neural networks
Dilated 1D-CNN
Temporal dependencies
Spatial dependencies
期刊
D
IF:
4.6
论文数:
249
被引数:
665
机构
引用论文
The impact of different procedures to arouse mortality awareness on various worldview dimensions
PREAI
分享收藏分享收藏分享收藏A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting基于cnn-gru的短期住宅负荷预测新方法.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Local Short and Middle Term Electricity Load Forecasting With Semi-Parametric Additive Models基于半参数可加模型的局部中短期电力负荷预测
PREAI
分享收藏分享收藏
查看更多内容