A systematic review of big data in energy analytics using energy computing techniques

In big data and machine learning, energy analytics has shown rapid development in the past decade. With this development in energy analytics, the energy data has exponentially increased well in different areas and domains. Energy computing techniques have been used in various domains, particularly i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Concurrency and computation Jg. 34; H. 4
Hauptverfasser: Dhanalakshmi, J., Ayyanathan, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 15.02.2022
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
artificial neural networks
Big Data
classification
clustering
Computation
Data analysis
data analytics
Datasets
Domains
Energy
energy analytics
energy computing
Indian electricity grid
Machine learning
Mathematical analysis
neural networks
regression
Smart grid
Structured data
Thermal power plants
time series analysis
Unstructured data
ISSN:1532-0626, 1532-0634
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In big data and machine learning, energy analytics has shown rapid development in the past decade. With this development in energy analytics, the energy data has exponentially increased well in different areas and domains. Energy computing techniques have been used in various domains, particularly in energy data to handle the data analysis process. This paper focuses on explaining the concept and evolution of various research questions formulation, data extraction, quality valuation, search strategy, study selection and reporting the results in energy‐oriented domains to evaluate energy‐computing techniques. The thermal power plant, power system and smart grid datasets were highly used out of plenty of other datasets discussed and the ANN algorithm is most commonly used to classify the dataset. Different sets of algorithms are used for data analysis, classification, converting structured data to unstructured data, regression, visualization, and forecasting. Journal publications from the year 2012 to 2020 have reviewed and adopted a systematic approach to identify the findings of all relevant research methodologies in the defined research domain.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6647