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Smart city article details
| Title | An Overview Of A New Statistical Non-Intrusive Load Monitoring (Nilm) Analysis And Recognition Approach For Domestic Environments: Denardo |
|---|---|
| ID_Doc | 8906 |
| Authors | Fazio P.; Mehic M.; Mannone M.; Astorino F.; Voznak M. |
| Year | 2024 |
| Published | 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 - Proceedings |
| DOI | http://dx.doi.org/10.1109/MetroLivEnv60384.2024.10615815 |
| Abstract | IP devices are ubiquitously spread, for both residential and industrial purposes, thanks to the low integration costs and rapid development cycle of all-IP-based 5G+ technologies. As a consequence, the engineering community now considers their automatization and energy scheduling/management as relevant research fields. These topics have a striking relevance also for the development of smart city networks. As a drawback, most ID-device applications produce a large amount of data (high-frequency complexity), requiring supervised machine learning algorithms to be properly analyzed. In this research, we focus on the performance of vehicular mobility and imaging systems, recognizing scenarios (with powered-on devices) in real-time, with the help of a simple convolutional neural network, proving the effectiveness of such an innovative low-cost approach. © 2024 IEEE. |
| Author Keywords | Appliance Classification; Convolutional Neural Networks; Data-to-Image Conversion; IoT Networks; Machine Learning |
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