用混合模型精准识别家庭光伏骗电行为，保障智能电网稳定
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
- 融合CNN、LSTM与Transformer捕捉长短时用电模式
- 结合温度数据提升对复杂窃电行为的检测准确率
- 适合智能电网安全与能源公平研究者参考
随着智能电网普及，智能城市面临日益严重的网络攻击和复杂电费欺诈行为，尤其在居民光伏发电系统中。传统电力窃电检测（ETD）方法难以捕捉复杂的时序依赖关系并融合多源数据，限制了检测效果。本文提出一种高效的ETD方法，可精准识别居民光伏系统中的欺诈行为，保障智能城市的供需平衡。所提混合深度学习模型结合多尺度卷积神经网络（CNN）、长短期记忆网络（LSTM）与变压器（Transformer），有效捕捉短时与长时依赖关系。此外，引入数据嵌入技术，将时间序列数据与离散温度变量无缝融合，增强检测鲁棒性。基于真实世界数据的大量仿真实验验证了该方法的有效性，显著提升了对复杂窃电行为的检测精度，为智能城市能源系统的稳定性与公平性提供支持。
原文摘要 · Abstract (English)
With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovoltaic (PV) generation systems. Traditional Electricity Theft Detection (ETD) methods often struggle to capture complex temporal dependencies and integrating multi-source data, limiting their effectiveness. In this work, we propose an efficient ETD method that accurately identifies fraudulent behaviors in residential PV generation, thus ensuring the supply-demand balance in smart cities. Our hybrid deep learning model, combining multi-scale Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, excels in capturing both short-term and long-term temporal dependencies. Additionally, we introduce a data embedding technique that seamlessly integrates time-series data with discrete temperature variables, enhancing detection robustness. Extensive simulation experiments using real-world data validate the effectiveness of our approach, demonstrating significant improvements in the accuracy of detecting sophisticated energy theft activities, thereby contributing to the stability and fairness of energy systems in smart cities.
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