融合多种技术提升电力中断预测精度
A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction
- 用PCA降维去噪，PR建模离散停电事件，Seq2Seq-LSTM捕捉长期依赖
- 在密歇根真实数据上，预测准确率显著优于现有方法
- 适合电力系统运维与智能电网研究者参考
精准预测电力中断是一项复杂任务，受天气、植被、野生动物和负荷波动等多种因素影响，这些因素引入了大量变异性和噪声，使可靠预测变得困难。长短期记忆网络（LSTM）作为一类循环神经网络，对非线性动态时间序列数据建模效果出色，已在股价预测、能源需求预测、需求响应及交通流管理中得到验证。本文提出一种混合深度学习框架——PCA-PR-Seq2Seq-Adam-LSTM，整合主成分分析（PCA）、泊松回归（PR）、序列到序列（Seq2Seq）架构与自适应矩估计优化的LSTM。PCA用于降低数据维度并稳定方差，泊松回归有效建模离散停电事件。Seq2Seq-Adam-LSTM组件通过高效的梯度优化和长期依赖捕捉能力增强时序特征学习。该框架在密歇根州真实停电记录上进行评估，结果表明，相比现有方法，所提方法显著提升了预测准确率与鲁棒性。
原文摘要 · Abstract (English)
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.
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