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.

翻译：窃电检测（ETD）与能耗预测（ECF）是智能电网系统中两个相互关联的挑战。共同应对这些问题对确保系统安全至关重要。本文针对智能电网系统中ETD与ECF的相互关联挑战展开研究。所提出的解决方案结合了长短期记忆网络（LSTM）与去噪扩散概率模型（DDPM），实现输入重构与预测。通过利用重构误差与预测误差，系统能够识别窃电行为，其中基于重构误差与基于预测误差的方法在不同类型攻击检测中相互补充。在真实数据集与合成数据集上的大量实验表明，该方案在ETD与ECF问题上均优于基线方法。集成方法显著提升了ETD性能，能够准确检测基线方法无法识别的窃电攻击。本研究为应对ETD与ECF挑战提供了一种全面有效的解决方案，在智能电网系统中展现了良好的性能与增强的安全性。