Aggregation architecture for data reduction and privacy in advanced metering infrastructure

Abstract

Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are used to deliver anonymized summary data to the central utility while preserving billing and automated connection services.

The Power of Big Data and Data Analytics for AMI Data: A Case Study

“…The privacy of customer data is also a significant challenge faced by developments around AMI data, which are consistently available with precise details about their consumption habits. In this sense, Foreman et al present a methodology to anonymize customer data with smart meters installed on their properties, while preserving the billing services and automatic connection to the centralized system of a utility [53].…”

An adaptive framework for real-time data reduction in AMI

“…In Zeinali and Thompson (2016) used two compression schemes; Adaptive Huffman(AH) scheme is used to compress data at SM layer and Lempel-Ziv Welsh (LZW) schema is used to compress data at aggregators layer. In Foreman and Pacheco (2016) applied in-network processing; the aggregators are responsible for not only collecting data from SMs but also analyzing, extracting information from collected data and transferring them to the utility server. However these works do not support real-time data reduction.…”