Energy theft detection (ETD) and energy consumption forecasting (ECF) are two
interconnected challenges in smart grid systems. Addressing these issues
collectively is crucial for ensuring system security. This paper addresses the
interconnected challenges of ETD and ECF in smart grid systems. The proposed
solution combines long short-term memory (LSTM) and a denoising diffusion
probabilistic model (DDPM) to generate input reconstruction and forecasting. By
leveraging the reconstruction and forecasting errors, the system identifies
instances of energy theft, with the methods based on reconstruction error and
forecasting error complementing each other in detecting different types of
attacks. Through extensive experiments on real-world and synthetic datasets,
the proposed scheme outperforms baseline methods in ETD and ECF problems. The
ensemble method significantly enhances ETD performance, accurately detecting
energy theft attacks that baseline methods fail to detect. The research offers
a comprehensive and effective solution for addressing ETD and ECF challenges,
demonstrating promising results and improved security in smart grid systems.

智能电网系统中的能量窃取检测和能量消费预测是两个相关的挑战。该研究提出了一种组合了长短期记忆和去噪扩散概率模型的解决方案，通过利用重建和预测误差，系统能够识别能量窃取的实例。在实验中，该方案在能量窃取检测和能量消费预测问题上优于基准方法，并且集成方法显著提高能量窃取检测的性能。该研究为解决能量窃取检测和能量消费预测挑战提供了全面有效的解决方案，展示了智能电网系统的良好结果和改进的安全性。