A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction

2025-09-20

AI Summary

Power outage prediction is challenged by strong noise and high variability arising from heterogeneous factors—including weather, vegetation, wildlife, and load fluctuations—limiting the robustness and accuracy of conventional methods. This paper proposes a multi-stage hybrid deep learning framework: (1) Principal Component Analysis (PCA) for dimensionality reduction of high-dimensional features; (2) Poisson regression to explicitly model the count-based nature of discrete outage events; and (3) a Seq2Seq architecture integrating LSTM networks optimized via the Adam algorithm to enhance temporal dependency modeling and long-horizon prediction stability. Evaluated on a real-world outage dataset from Michigan, the framework achieves an 18.7% reduction in average prediction error over state-of-the-art baselines and demonstrates superior robustness under extreme weather conditions. The approach delivers an interpretable, production-ready paradigm for distribution grid resilience assessment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Ensemble Methods

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurately forecasting power outages is a complex task influenced by diverse factors such as weather conditions [1], vegetation, wildlife, and load fluctuations. These factors introduce substantial variability and noise into outage data, making reliable prediction challenging. Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN), are particularly effective for modeling nonlinear and dynamic time-series data, with proven applications in stock price forecasting [2], energy demand prediction, demand response [3], and traffic flow management [4]. This paper introduces a hybrid deep learning framework, termed PCA-PR-Seq2Seq-Adam-LSTM, that integrates Principal Component Analysis (PCA), Poisson Regression (PR), a Sequence-to-Sequence (Seq2Seq) architecture, and an Adam-optimized LSTM. PCA is employed to reduce dimensionality and stabilize data variance, while Poisson Regression effectively models discrete outage events. The Seq2Seq-Adam-LSTM component enhances temporal feature learning through efficient gradient optimization and long-term dependency capture. The framework is evaluated using real-world outage records from Michigan, and results indicate that the proposed approach significantly improves forecasting accuracy and robustness compared to existing methods.

Problem

• Forecasting power outages with complex influencing factors
• Modeling nonlinear dynamic time-series data for prediction
• Improving accuracy and robustness in outage forecasting

Innovation

• PCA reduces dimensionality and stabilizes data variance
• Poisson Regression models discrete power outage events
• Seq2Seq-Adam-LSTM captures long-term temporal dependencies