Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation wi...

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Detalles Bibliográficos
Autores principales: Kasaraneni, Purna Prakash, Venkata Pavan Kumar, Yellapragada, Moganti, Ganesh Lakshmana Kumar, Kannan, Ramani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741071/
https://www.ncbi.nlm.nih.gov/pubmed/36502025
http://dx.doi.org/10.3390/s22239323

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741071/
https://www.ncbi.nlm.nih.gov/pubmed/36502025
http://dx.doi.org/10.3390/s22239323

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