scholarly journals A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5650
Author(s):  
Jenniffer S. Guerrero-Prado ◽  
Wilfredo Alfonso-Morales ◽  
Eduardo F. Caicedo-Bravo
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Smart Grids ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Advanced Metering Infrastructure ◽  
Data Interoperability ◽  
Architecture Model ◽  
Data Framework ◽  
Advanced Metering

The Advanced Metering Infrastructure (AMI) data represent a source of information in real time not only about electricity consumption but also as an indicator of other social, demographic, and economic dynamics within a city. This paper presents a Data Analytics/Big Data framework applied to AMI data as a tool to leverage the potential of this data within the applications in a Smart City. The framework includes three fundamental aspects. First, the architectural view places AMI within the Smart Grids Architecture Model-SGAM. Second, the methodological view describes the transformation of raw data into knowledge represented by the DIKW hierarchy and the NIST Big Data interoperability model. Finally, a binding element between the two views is represented by human expertise and skills to obtain a deeper understanding of the results and transform knowledge into wisdom. Our new view faces the challenges arriving in energy markets by adding a binding element that gives support for optimal and efficient decision-making. To show how our framework works, we developed a case study. The case implements each component of the framework for a load forecasting application in a Colombian Retail Electricity Provider (REP). The MAPE for some of the REP’s markets was less than 5%. In addition, the case shows the effect of the binding element as it raises new development alternatives and becomes a feedback mechanism for more assertive decision making.

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

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3289
Author(s):  
Jenniffer Sidney Guerrero-Prado ◽  
Wilfredo Alfonso-Morales ◽  
Eduardo Caicedo-Bravo ◽  
Benjamín Zayas-Pérez ◽  
Alfredo Espinosa-Reza
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Smart Grids ◽  
Data Analytics ◽  
Raw Data ◽  
Industrial Sectors ◽  
Advanced Metering ◽  
Data Driven Decisions ◽  
City Of London

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid.

scholarly journals Quality Control Methods for Advanced Metering Infrastructure Data

Smart Cities ◽  
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.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

scholarly journals A multi-level optimization technique for electricity consumption management and pricing in residential neighborhoods

2021 ◽  
Author(s):  
Ramyar Rashed Mohassel
Keyword(s):  
Smart Grids ◽  
New Technologies ◽  
Power Transmission ◽  
Optimization Technique ◽  
Electricity Consumption ◽  
Optimization Method ◽  
Residential Neighborhood ◽  
Upper Level ◽  
Advanced Metering ◽  
Power Management Unit

With the introduction of new technologies, concepts and approaches in power transmission, distribution and utilization such as Smart Grids (SG), Advanced Metering Infrastructures (AMI), Distributed Energy Resources (DER) and Demand Side Management (DSM), new capabilities have emerged that enable efficient use and management of power consumption. These capabilities are applicable at micro level in households and building complexes as well as at macro level for utility providers in form of resource and revenue management initiatives. On the other hand, integration of Information Technology (IT) and instrumentation has brought Building Management Systems (BMS) to our homes and has made it possible for the ordinary users to take advantage of more complex and sophisticated energy and cost management features as an integral part of their BMS. The idea of combining capabilities and advantages offered by SG, AMI, DER, DSM and BMS is the backbone of this thesis and has resulted in developing a unique, two-level optimization method for effective deployment of DSM at households and residential neighborhoods. The work consists of an optimization algorithm for households to maximize utilization of DER as the lower level of the envisioned two-level optimization technique while using a customized Game Theoretic optimization for optimizing revenue of utility providers for residential neighborhood as the upper level. This work will also introduce a power management unit, called Load Moderation Center (LMC), to host the developed optimization algorithms as an integrated part of BMS. LMC, upon successful completion, will be able to automatically plan consumption, effectively utilize available sources including grid, renewable energies and storages, and eliminate the need for residences to manually program their BMS for different market scenarios.

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