Utilizing Advanced Metering Infrastructure to Build a Public Key Infrastructure for Electric Vehicles

The conventional public key infrastructure (PKI) model, which powers most of the Internet, suffers from an excess of trust into certificate authorities (CAs), compounded by a lack of transparency which makes it vulnerable to hard-to-detect targeted stealth impersonation attacks. Existing approaches to make certificate issuance more transparent, including ones based on blockchains, are still somewhat centralized. We present decentralized PKI transparency (DPKIT): a decentralized client-based approach to enforcing transparency in certificate issuance and revocation while eliminating single points of failure. DPKIT efficiently leverages an existing blockchain to realize an append-only, distributed associative array, which allows anyone (or their browser) to audit and update the history of all publicly issued certificates and revocations for any domain. Our technical contributions include definitions for append-only associative ledgers, a security model for certificate transparency, and a formal analysis of our DPKIT construction with respect to the same. Intended as a client-side browser extension, DPKIT will be effective at fraud detection and prosecution, even under fledgling user adoption, and with better coverage and privacy than federated observatories, such as Google’s or the Electronic Frontier Foundation’s.

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Quality Control Methods for Advanced Metering Infrastructure Data

Smart Cities ◽

10.3390/smartcities4010012 ◽

2021 ◽

Vol 4 (1) ◽

pp. 195-203

Author(s):

Eric Garrison ◽

Joshua New

Keyword(s):

Quality Control ◽

Missing Values ◽

Electricity Consumption ◽

Empirical Validation ◽

Advanced Metering Infrastructure ◽

Building Energy Modeling ◽

Building Type ◽

Industry Standard ◽

Advanced Metering ◽

Electrical Consumption

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

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