Abstract
Full utilization of renewable energy resources is a difficult task due to the constantly changing demand-side load of the electrical grid. Demand-side management would solve this crucial problem. To enable demand-side management, knowledge about the composition of the grid load is required, as well as the ability to schedule individual loads. There are proposed Smart Plugs presented in the literature capable of such tasks. The problem, however, is that these methods lack the ability to detect if a previously unseen electrical load is connected. Misclassification of such loads presents a problem for load estimation and scheduling. Open-set recognition methods solve this problem by providing a way to detect samples not belonging to any class used during the training of the classifier. This paper evaluates the novel application of open-set recognition methods to the problem of load classification. Two approaches were examined, and both offer promising results. A Support Vector Machine based approach was first evaluated. The second, more robust method used a modified OpenMax-based algorithm to detect unseen loads.
Funder
New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund
Hungarian National Research, Development and Innovation Office
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference26 articles.
1. Németh, D., and Tornai, K. (2022, January 27–29). SP4LC: A Method for Recognizing Power Consumers in a Smart Plug. Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems-SMARTGREENS, INSTICC, SciTePress, Online.
2. Németh, D.I., and Tornai, K. (2022, January 10–12). Detecting Unknown Electrical Loads Using open-set recognition. Proceedings of the 2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada.
3. Nemeth, D.I., and Tornai, K. (2023). Proceedings of the Smart Cities, Green Technologies, and Intelligent Transport Systems, Springer International Publishing.
4. Bendale, A., and Boult, T.E. (2016, January 27–30). Towards Open Set Deep Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.
5. Classification of Energy Consumption in Buildings With Outlier Detection;Li;IEEE Trans. Ind. Electron.,2010