EXTENDED APPLIED DATA CLEANING METHODS IN OUTLIER DETECTION FOR RESIDENTIAL CONSUMER

Abstract

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.

Keywords

• energy measurement
• artificial intelligence
• statistics
• probabilistic computing
• data science
• data aggregation
• algorithms
• energy efficiency