Advanced metering infrastructure (AMI)is a critical part of a modern smart grid that performs the bidirectional data flow of sensitive power information such as smart metering data and control commands. The real-time monitoring and control of the grid are ensured through AMI. While smart meter data helps to improve the overall performance of the grid in terms of efficient energy management, it has also made the AMI an attractive target of cyber attackers with a goal of stealing energy. This is performed through the physical or cyber tampering of the meters, as well as by manipulating the network infrastructure to alter collected data. Proper technology is required for the identification of energy fraud. In this paper, we propose a novel technique to detect fraudulent data from smart meters based on the energy consumption patterns of the consumers by utilizing deep learning techniques. We also propose a method for detecting the suspicious relay nodes in the AMI infrastructure that may manipulate the data while forwarding it to the aggregators. We present the performance of our proposed technique, which shows the correctness of the models in identifying the suspicious smart meter data.
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DP-AMI-FL: Secure Framework for Machine Learning-based AMI Applications
Machine Learning (ML) algorithms have shown quite promising applications in smart meter data analytics enabling intelligent energy management systems for the Advanced Metering Infrastructure (AMI). One of the major challenges in developing ML applications for the AMI is to preserve user privacy while allowing active end-users participation. This paper addresses this challenge and proposes Differential Privacy-enabled AMI with Federated Learning (DP-AMI-FL), framework for ML-based applications in the AMI. This framework provides two layers of privacy protection: first, it keeps the raw data of consumers hosting ML applications at edge devices (smart meters) with Federated Learning (FL), and second, it obfuscates the ML models using Differential Privacy (DP) to avoid privacy leakage threats on the models posed by various inference attacks. The framework is evaluated by analyzing its performance on a use case aimed to improve Short-Term Load Forecasting (STLF) for residential consumers having smart meters and home energy management systems. Extensive experiments demonstrate that the framework when used with Long Short-Term Memory (LSTM) recurrent neural network models, achieves high forecasting accuracy while preserving users data privacy.
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- NSF-PAR ID:
- 10418699
- Date Published:
- Journal Name:
- 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/isgt51731.2023.10066415
