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
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 Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects.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
683682Informations 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.
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