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：窃电检测与能耗预测是智能电网系统中两个相互关联的挑战，协同解决这些问题对保障系统安全至关重要。本文针对智能电网系统中窃电检测与能耗预测的互联问题，提出了一种结合长短期记忆网络与去噪扩散概率模型的方法，用于实现输入重构和能耗预测。通过利用重构误差与预测误差，系统可识别窃电行为，其中基于重构误差与预测误差的方法在检测不同类型攻击时相互补充。基于真实数据集与合成数据集的广泛实验表明，所提方案在窃电检测和能耗预测任务中均优于基线方法。集成方法显著提升了窃电检测性能，能够准确识别基线方法无法检测的窃电攻击。本研究为应对窃电检测与能耗预测挑战提供了全面有效的解决方案，在智能电网系统中展现了良好的应用前景与安全性能提升。