An Effective Communication Performance Index for Advanced Metering Infrastructure

Author(s):  
Chia-Wei Chao ◽  
Jen-Hao Teng ◽  
Bin-Han Liu ◽  
Wei-Hao Huang ◽  
Jih-Ching Chiu
Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 88 ◽  
Author(s):  
Jen-Hao Teng ◽  
Chia-Wei Chao ◽  
Bin-Han Liu ◽  
Wei-Hao Huang ◽  
Jih-Ching Chiu

Advanced Metering Infrastructure (AMI), the foundation of smart grids, can be used to provide numerous intelligent power applications and services based on the data acquired from AMI. Effective and efficient communication performance between widely-spread smart meters and Data Concentrator Units (DCUs) is one of the most important issues for the successful deployment and operation of AMI and needs to be further investigated. This paper proposes an effective Communication Performance Index (CPI) to assess and supervise the communication performance of each smart meter. Some communication quality measurements that can be easily acquired from a smart meter such as reading success rate and response time are used to design the proposed CPI. Fuzzy logic is adopted to combine these measurements to calculate the proposed CPI. The CPIs for communication paths, DCUs and whole AMI can then be derived from meter CPIs. Simulation and experimental results for small-scale AMIs demonstrate the validity of the proposed CPI. Through the calculated CPIs, the communication performance and stability for AMI can be effectively assessed and supervised.


Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 195-203
Author(s):  
Eric Garrison ◽  
Joshua New

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.


2021 ◽  
pp. 1-1
Author(s):  
Wen Tian ◽  
Miao Du ◽  
Xiaopeng Ji ◽  
Guangjie Liu ◽  
Yuewei Dai ◽  
...  

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