Energformer: A New Transformer Model for Energy Disaggregation

Georgios-Fotios Angelis, Christos Timplalexis, Athanasios I. Salamanis, Stelios Krinidis, Dimosthenis Ioannidis, Dionysios D. Kehagias, Dimitrios Tzovaras

Abstract: In recent years, a lot of progress has been reported in the field of energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM). Despite the fact that there are many studies focusing on the residential sector, there is considerably less research interest for the industrial sector. In this paper, we present a deep neural network based on Transformers, targeted towards capturing complex patterns in long sequences of data. The proposed transformer architecture employs 1D spatial convolutions in self-attention, and modifications inside the attention computations manage to reduce computational complexity without any loss in predictive accuracy. In order to evaluate the performance of the proposed deep learning architecture, a set of experiments has been conducted using a publicly available dataset. The experimental results indicate that the proposed model achieves better disaggregation accuracy compared to other state-of-the-art NILM models.