Smart City Gnosys
Smart city article details
| Title | Load Monitoring And Appliance Recognition Using An Inexpensive, Low-Frequency, Data-To-Image, Neural Network, And Network Mobility Approach For Domestic Iot Systems |
|---|---|
| ID_Doc | 35428 |
| Authors | Fazio P.; Mehic M.; Voznak M. |
| Year | 2024 |
| Published | IEEE Internet of Things Journal, 11, 8 |
| DOI | http://dx.doi.org/10.1109/JIOT.2023.3340423 |
| Abstract | With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study's simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances. © 2014 IEEE. |
| Author Keywords | Appliance classification; convolutional neural networks; data-to-image conversion; Internet of Things (IoT) networks; machine learning (ML) |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
8906  | 0.938 | Fazio P.; Mehic M.; Mannone M.; Astorino F.; Voznak M. | An Overview Of A New Statistical Non-Intrusive Load Monitoring (Nilm) Analysis And Recognition Approach For Domestic Environments: Denardo | 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024 - Proceedings (2024) |
| 32368 | 0.87 | Ulloa-Vasquez F.; Garcia-Santander L.; Carrizo D.; Heredia-Figueroa V. | Intelligent Electrical Pattern Recognition Of Appliances Consumption For Home Energy Management Using High Resolution Measurement | IEEE Latin America Transactions, 20, 2 (2022) |
| 29531 | 0.865 | Li S.; Zhao S.; Gao P.; Ge S.; Huang R.; Shu L. | Hp-Mobilenetv3: A Lightweight Neural Network For Non-Intrusive Load Monitoring | Engineering Research Express, 7, 1 (2025) |
| 47072 | 0.86 | Wang F.; Hou S.; Gao J.; Wu D. | Rotating Kernel Cnn Optimization For Efficient Iot Surveillance On Low-Power Devices | Electronics (Switzerland), 13, 14 (2024) |
| 1332 | 0.855 | Babuji R.; Pious A.E.; Vinu A.T.; Devi V.; Thiripurasundari D.; Kumar S.S. | A Deep Learning Approach For Intelligent Iot Based Energy Management System | International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022 - Proceedings (2022) |
| 23493 | 0.852 | Mani T.; Charith B.; Venkatamuni T.; Vaishali M.; Lalitha K.; Muthuraj B. | Energy-Efficient Iot Solutions For Smart Cities | Proceedings - 1st International Conference on Frontier Technologies and Solutions, ICFTS 2025 (2025) |