Data Consistency for Data-Driven Smart Energy Assessment.

big data data analytics data-driven internet of things knowledge extraction machine learning smart energy uncertainty

Journal

Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603

Informations de publication

Date de publication:
2021
Historique:
received: 21 03 2021
accepted: 19 04 2021
entrez: 31 5 2021
pubmed: 1 6 2021
medline: 1 6 2021
Statut: epublish

Résumé

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects.

Identifiants

pubmed: 34056585
doi: 10.3389/fdata.2021.683682
pmc: PMC8155608
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

683682

Informations de copyright

Copyright © 2021 Chicco.

Déclaration de conflit d'intérêts

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

IEEE Trans Syst Man Cybern B Cybern. 2004 Feb;34(1):95-109
pubmed: 15369055
IEEE Trans Syst Man Cybern B Cybern. 1998;28(1):103-9
pubmed: 18255928
Pattern Recognit Lett. 2018 Dec 1;116:88-96
pubmed: 30416234

Auteurs

Gianfranco Chicco (G)

Dipartimento Energia "Galileo Ferraris," Politecnico di Torino, Torino, Italy.

Classifications MeSH