Document Type
Journal Article
Publication Title
Energies
Volume
15
Issue
17
Publisher
MDPI
School
School of Engineering
RAS ID
45476
Abstract
In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data.
DOI
10.3390/en15176241
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Ashraf, M. M., Waqas, M., Abbas, G., Baker, T., Abbas, Z. H., & Alasmary, H. (2022). FedDP: A privacy-protecting theft detection scheme in smart grids using federated learning. Energies, 15(17), 6241. https://doi.org/10.3390/en15176241