EXTENDED APPLIED DATA CLEANING METHODS IN OUTLIER DETECTION FOR RESIDENTIAL CONSUMER

Autor:	Dacian I. JURJ, Dan D. MICU, Alexandru G. BERCIU, Mircea LANCRAJAN, Levente CZUMBIL, Andrei BENDE, Bogdan A. MITRACHE, Alexandru MUREȘAN
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	energy measurement artificial intelligence statistics probabilistic computing data science data aggregation algorithms energy efficiency Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Carpathian Journal of Electrical Engineering, Vol 17, Iss 1, Pp 21-31 (2023)
Druh dokumentu:	article
ISSN:	1843-7583
Popis:	This paper delves into the subject of outlier detection techniques tailored for unique datasets related to residential energy consumption. Building upon the current state of research we introduce the Grubbs and Z-score methods and investigate a range of outlier detection strategies encompassing statistical, probabilistic, and machine learning algorithms. The findings underscore the importance of outlier detection in the Romanian residential energy sector.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f3ee8e9e5bd047b3b20c12f64c0fadd0 Zobrazit plný text záznamu View record in DOAJ