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The Power of Big Data and Data Analytics for AMI Data: A Case Study
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 utilit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309066/ https://www.ncbi.nlm.nih.gov/pubmed/32526976 http://dx.doi.org/10.3390/s20113289 |
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author | Guerrero-Prado, Jenniffer Sidney Alfonso-Morales, Wilfredo Caicedo-Bravo, Eduardo Zayas-Pérez, Benjamín Espinosa-Reza, Alfredo |
author_facet | Guerrero-Prado, Jenniffer Sidney Alfonso-Morales, Wilfredo Caicedo-Bravo, Eduardo Zayas-Pérez, Benjamín Espinosa-Reza, Alfredo |
author_sort | Guerrero-Prado, Jenniffer Sidney |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7309066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73090662020-06-25 The Power of Big Data and Data Analytics for AMI Data: A Case Study Guerrero-Prado, Jenniffer Sidney Alfonso-Morales, Wilfredo Caicedo-Bravo, Eduardo Zayas-Pérez, Benjamín Espinosa-Reza, Alfredo Sensors (Basel) Article 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. MDPI 2020-06-09 /pmc/articles/PMC7309066/ /pubmed/32526976 http://dx.doi.org/10.3390/s20113289 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guerrero-Prado, Jenniffer Sidney Alfonso-Morales, Wilfredo Caicedo-Bravo, Eduardo Zayas-Pérez, Benjamín Espinosa-Reza, Alfredo The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title | The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title_full | The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title_fullStr | The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title_full_unstemmed | The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title_short | The Power of Big Data and Data Analytics for AMI Data: A Case Study |
title_sort | power of big data and data analytics for ami data: a case study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309066/ https://www.ncbi.nlm.nih.gov/pubmed/32526976 http://dx.doi.org/10.3390/s20113289 |
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