Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA

     

Disaggregation of digital meter data for synthetic load profile generation

Toon Bogaerts 2,3, Stef Jacobs 1, Sara Ghane 2,3, Freek Van Riet 1, Wim Casteels 2,3, Siegfried Mercelis 1, Ivan Verhaert 2, Peter Hellinckx 1
1 Energy and Materials in Infrastructure and Buildings, University of Antwerp, Belgium
2 IDLab, University of Antwerp, Belgium
3 Imec, Belgium


DOI: https://doi.org/10.26868/25222708.2021.30236
Abstract: The electrical consumption has to be taken into account in building simulations. Empirically-based profiles are required, which can be generated by central measurements and using non-intrusive load monitoring (NILM) for disaggregation. In this work, we present an overview of NILM techniques, a comparison between two frequently used deep neural networks for individual appliance identification and we investigate the influence of the sampling rate with regards to the accuracy. Our best performing neural network is a combination of convolution and long-short-term memory networks. Furthermore, the sampling rate has a significant influence on the performance of neural networks in this context. There should be a trade-off between sampling rate and efficiency when applied in real-world devices.
Keywords: Non Intrusive Load Monitoring, Smart meters, Data disaggregation
Pages: 1271 - 1278
Paper:
bs2021_30236