A Reliability Prediction Model For Power Outage Planning In Distribution Networks Combining Genetic Algorithms And LSTM
Abstract
Distribution network power outages are frequent and unpredictable, threatening infrastructure reliability and energy security. Traditional models with static risk evaluations or rule-based logic cannot adjust to complex spatiotemporal outage patterns. This research presents GAPO-LSTM (Genetic Algorithm–Powered Outage prediction with LSTM), a novel hybrid reliability prediction model that integrates GA optimization with LSTM sequence modeling. This study aims to create a comprehensive, data-driven system that correctly forecasts localized power failures and improves distribution network outage planning. The model dynamically learns from previous outage records and optimizes input characteristics and LSTM hyperparameters for forecasting accuracy. The suggested method preprocesses and geographically clusters power outage data to group similar locations. Genetic algorithms choose ideal feature subsets, sequence lengths, and learning parameters for LSTMs. The attention-enhanced LSTM network is trained on temporal outage sequences to predict cluster outage risks. In high-risk zone classification, GAPO-LSTM reduces RMSE by 18.6% and increases F1-score by 12.4%, outperforming vanilla LSTM and Random Forest. Sensitivity research shows the model's resilience to cluster density and outage volumes. Experimental results show the model can learn outage dynamics in large-scale distribution contexts while scaling. Finally, GAPO-LSTM provides a powerful, intelligent solution for proactive outage management and reliability improvement in modern power systems.References
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https://www.kaggle.com/datasets/hemishveeraboina/maryland-power-outage-a-geographic-dataset
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