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 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.

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

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

2021 ◽  
pp. 1-24
Author(s):  
Amine Belhadi ◽  
Sachin S. Kamble ◽  
Angappa Gunasekaran ◽  
Karim Zkik ◽  
Dileep Kumar M. ◽  
...  
Keyword(s):  
Big Data ◽  
Six Sigma ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Lean Six Sigma ◽  
Chemical Company ◽  
Green Performance

What Can Fitness Apps Teach Us About Group Privacy?

2021 ◽  
pp. 1-30
Author(s):  
Miriam J. Metzger ◽  
Jennifer Jiyoung Suh ◽  
Scott Reid ◽  
Amr El Abbadi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Individual Privacy ◽  
Recent Advances

This chapter begins with a case study of Strava, a fitness app that inadvertently exposed sensitive military information even while protecting individual users' information privacy. The case study is analyzed as an example of how recent advances in algorithmic group inference technologies threaten privacy, both for individuals and for groups. It then argues that while individual privacy from big data analytics is well understood, group privacy is not. Results of an experiment to better understand group privacy are presented. Findings show that group and individual privacy are psychologically distinct and uniquely affect people's evaluations, use, and tolerance for a fictitious fitness app. The chapter concludes with a discussion of group-inference technologies ethics and offers recommendations for fitness app designers.

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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