August 22, 2025EntropyOpen Access

Fault Recovery Strategy with Net Load Forecasting Using Bayesian Optimized LSTM for Distribution Networks

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This model improves net load forecasting and reduces network losses in distribution networks, suggesting efficient fault recovery methods.

The proposed strategy enhances fault recovery by optimizing net load forecasting in distribution networks, achieving a significant reduction in network losses.
Key performance metrics include a net load forecasting R2 of 0.9569 and a reduction of network losses by 33.2% after implementation.
The approach utilizes a hybrid optimization method, specifically a genetic algorithm enhanced with quantum particle swarm optimization, for greater solution accuracy.
Simulation results indicate improved load recovery rates from 72.7% to 75.8% and extended power supply duration from 60 to 120 minutes, supporting efficient restoration.

Ding et al. (2025) studied this question.

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This model improves net load forecasting and reduces network losses in distribution networks, suggesting efficient fault recovery methods.