  Energy theft, characterized by manipulating energy consumption readings to
reduce payments, poses a dual threat-causing financial losses for grid
operators and undermining the performance of smart grids. Effective Energy
Theft Detection (ETD) methods become crucial in mitigating these risks by
identifying such fraudulent activities in their early stages. However, the
majority of current ETD methods rely on supervised learning, which is hindered
by the difficulty of labelling data and the risk of overfitting known attacks.
To address these challenges, several unsupervised ETD methods have been
proposed, focusing on learning the normal patterns from honest users,
specifically the reconstruction of input. However, our investigation reveals a
limitation in current unsupervised ETD methods, as they can only detect
anomalous behaviours in users exhibiting regular patterns. Users with
high-variance behaviours pose a challenge to these methods. In response, this
paper introduces a Denoising Diffusion Probabilistic Model (DDPM)-based ETD
approach. This innovative approach demonstrates impressive ETD performance on
high-variance smart grid data by incorporating additional attributes correlated
with energy consumption. The proposed methods improve the average ETD
performance on high-variance smart grid data from below 0.5 to over 0.9 w.r.t.
AUC. On the other hand, our experimental findings indicate that while the
state-of-the-art ETD methods based on reconstruction error can identify ETD
attacks for the majority of users, they prove ineffective in detecting attacks
for certain users. To address this, we propose a novel ensemble approach that
considers both reconstruction error and forecasting error, enhancing the
robustness of the ETD methodology. The proposed ensemble method improves the
average ETD performance on the stealthiest attacks from nearly 0 to 0.5 w.r.t.
5%-TPR.
