Sensing Application for Non-intrusive Load Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: closed (15 October 2020) | Viewed by 263
Special Issue Editor
Interests: AI; AI security; cryptography; intrusion detection system; information security; IoT security; blockchain and security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is my pleasure to invite submissions to a Special Issue of the journal Sensors on the topic of “Non-Intrusive Load Monitoring Using Sensor Signal and Information Processing”. Non-intrusive load monitoring (NILM) using sensor signal and information processing is an underlying field of research focusing on the mathematical foundations and practical appliances of signal processing algorithms that learn, reason, and act in smart energy monitoring. In NILM, energy disaggregation is evolving toward a more and more interesting topic. However, it requires an efficient technique to build real applications for control and manage efficient home energy consumption. In particular, the application of signal processing techniques yields an enhancement in the recognition of performance of household appliances based on standard hardware through the addition of computational capability. In order to achieve the goal, proper modeling of the devices or systems should be obtained, thus allowing to develop optimized algorithms which are suitable to the specific application.
With this Special Issue, we would like to encourage original contributions regarding recent developments and ideas in non-intrusive load monitoring. Potential topics include but are not limited to: ·
- Data science and analytics for big data;·
- Event or non-event detection;·
- Signal processing for smart grid, load forecasting, and energy management;·
- Machine learning for signal and image processing;·
- Deep-learning-based neural network;·
- Generative neural network (GAN);·
- Transfer learning;Federate learning.
Prof. Howon Kim
Guest Editor
Manuscript Submission Information
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Keywords
- Household appliance
- Energy disaggregation
- Monitoring systems
- Image processing
- Signal processing
- Event and non-event detection
- Machine learning and deep learning
- GAN
- Transfer learning
- Federate learning
