What question did this study set out to answer?

The aim is to accurately predict line loss rates in distribution networks for short-term forecasts.

February 2, 2026Open Access

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

Key Points

The aim is to accurately predict line loss rates in distribution networks for short-term forecasts.
Developed an attention-GCN-LSTM model integrating GCN, LSTM, and a three-level attention mechanism.
Evaluated the model using real-world data from 10 kV feeders.
Focused on short-term forecasts ranging from 1 hour to 1 week.
Achieved superior prediction accuracy compared to existing algorithms.
Effectively captured spatial and temporal dependencies for improved forecasting.
Outperformed in multihorizon forecasting scenarios.

Abstract

ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level attention mechanism to address this challenge. By effectively capturing spatial and temporal dependencies, our model enables precise forecasting of line loss rates across multiple horizons. Comprehensive evaluations using real‐world data from 10 kV feeders demonstrate that the attention‐GCN–LSTM model consistently outperforms existing algorithms, achieving superior prediction accuracy and excelling in multihorizon forecasting. This model holds significant potential for improving line loss management in distribution networks.

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