Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 2, 2025ElectricityOpen Access

A Hierarchical RNN-LSTM Model for Multi-Class Outage Prediction and Operational Optimization in Microgrids

View Full Paper
Ask AI
Bookmark
Share

Authors

NLNouman LiaqatMZMuhammad ZubairAWAashir Waleed

Discussion

Loading...

Member takes

Overview

Machine learning demonstrates improved outage prediction in microgrids, indicating better operational efficiency.

Key Points

  • The model achieved an accuracy of 86.52% on a real-time dataset, enhancing outage detection.
  • Precision at 86% and recall at 86.20% indicate high performance in identifying outages.
  • Data preprocessing ensured effective handling of historical and real-time data for accurate predictions.
  • The approach leveraging temporal context improves operational optimization in microgrid systems.

Cite This Study

Liaqat et al. (2025) studied this question.

synapsesocial.com/papers/68de6f3f83cbc991d0a22ab5https://doi.org/10.3390/electricity6040055
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Short-Term Forecasting of Unplanned Power Outages Using Machine Learning Algorithms: A Robust Feature Engineering Strategy Against Multicollinearity and Nonlinearity2025 · 3 citations
  2. 2Smart Home Energy Consumption Forecasting Using LSTM2025
  3. 3Hybrid Deep Learning Models for Energy Consumption Forecasting: A CNN-LSTM Approach for Large-Scale Datasets2025
  4. 4A Spatiotemporal Deep Learning Framework for Joint Load and Renewable Energy Forecasting in Stability-Constrained Power Systems2025
  5. 5Optimization of Smart Home Energy Consumption Using Machine Learning-Based Load Forecasting2025