| Autor: |
Toon Bogaerts, Stef Jacobs, Sara Ghane, Freek Van Riet, Wim Casteels, Siegfried Mercelis, Ivan Verhaert, Peter Hellinckx |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
International Building Performance Simulation Association (IBPSA) : proceedings of Building Simulation 17th International Conference of IBPSA (BS 2021), 1-3 September, 2021, Bruges, Belgium |
| ISSN: |
2522-2708 |
| DOI: |
10.26868/25222708.2021.30236 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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