Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution
  Network Using Attention-GCN-LSTM
Jie Liu Yijia Cao Yong Li Yixiu Guo Wei Deng
Accurately predicting line loss rates is vital for effective line loss
management in distribution networks, especially over short-term multi-horizons
ranging from one hour to one week. In this study, we propose
Attention-GCN-LSTM, a novel method that combines Graph Convolutional Networks
(GCN), Long Short-Term Memory (LSTM), and a three-level attention mechanism to
address this challenge. By capturing spatial and temporal dependencies, our
model enables accurate forecasting of line loss rates across multiple horizons.
Through comprehensive evaluation using real-world data from 10KV feeders, our
Attention-GCN-LSTM model consistently outperforms existing algorithms,
exhibiting superior performance in terms of prediction accuracy and
multi-horizon forecasting. This model holds significant promise for enhancing
line loss management in distribution networks.