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

TL;DR

This paper introduces Attention-GCN-LSTM, a novel deep learning model combining GCN, LSTM, and attention mechanisms to accurately forecast line loss rates in distribution networks across multiple short-term horizons.

Contribution

The paper presents a new Attention-GCN-LSTM model that effectively captures spatial-temporal dependencies for multi-horizon line loss rate forecasting in distribution networks.

Findings

Outperforms existing algorithms in accuracy

Effective across multiple forecasting horizons

Validated on real-world 10KV feeder data

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.