A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data

• Published in: Energy and buildings Vol. 231; p. 110601
• Main Authors: Liu, Xue, Ding, Yong, Tang, Hao, Xiao, Feng
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.01.2021; Elsevier BV
• Subjects: Algorithms; Anomalies; Building energy management; Cluster analysis; Clustering; Data analysis; Data mining; Decision tree; Electricity; Electricity consumption; Electricity usage pattern; Energy consumption; Energy management; Energy usage; Fault detection; Knowledge discovery; Office buildings; Outliers (statistics); Regression analysis; Statistical analysis; Time series clustering; Use statistics; Knowledge discovery; Data mining; Building energy management; Time series clustering; Electricity usage pattern; Decision tree

•We propose a general data mining-based framework for mining real-time building electricity consumption data.•The framework aims to identify typical electricity load patterns and discover insightful knowledge hidden in the patterns.•Applications of the framework in three practical office buildings a...