O. Krystalakos, C. Nalmpantis, and D. Vrakas. 2018. Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN ’18). Association for Computing Machinery, New York, NY, USA, Article 7, 1–6. DOI:https://doi.org/10.1145/3200947.3201011
Author(s): O. Krystalakos, C. Nalmpantis, and D. Vrakas
Keywords: energy disaggregation, non-intrusive load monitoring, artificial neural networks
Abstract: Energy disaggregation is the process of extracting the power consumptions of multiple appliances from the total consumption signal of a building. Artificial Neural Networks (ANN) have been very popular for this task in the last decade. In this paper we propose two recurrent network architectures that use sliding window for real-time energy disaggregation. We compare this approach to existing techniques using six metrics and find that it scores better for multi-state devices. Finally, we compare ANNs that use Gated Recurrent Unit neurons against those using Long Short-Term Memory neurons and find that they perform equally.