Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM

• URL: http://arxiv.org/abs/2312.11898v1
• Date: Tue, 19 Dec 2023 06:47:22 GMT
• Title: Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM
• Authors: Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, and Wei Deng
• Abstract summary: 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. Our model enables accurate forecasting of line loss rates across multiple horizons.
• Score: 9.460123100630158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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