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
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2020.110601

•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 are presented.•The framework is potentially used for an early fault detection of anomalous electricity load profiles. With the development of advanced information techniques, smart energy meters have made a considerable amount of real-time electricity consumption data available. These data provide a promising way to understand energy usage patterns and improve building energy management. However, previous studies have paid more attention to methodologies for the identification of energy usage patterns and are limited in the interpretability and applications of the patterns. In this context, this paper proposes a general data mining-based framework that can extract typical electricity load patterns (TELPs) and discover insightful information hidden in the patterns. The framework integrates multiple data mining techniques and mainly consists of three phases: data preparation, identification of TELPs and knowledge discovery in the patterns. A new clustering method with a two-step clustering analysis is proposed to identify the TELPs at the individual building level. Before clustering, five statistical features that represent the shapes of electricity load profiles are first defined to reduce the dimensions of daily electricity load profiles. The first clustering step aims at detecting outliers of daily electricity load profiles (DELPs) by using the density-based spatial clustering application with noise (DBSCAN) algorithm clustering technique, which addresses the data quality issues for electricity consumption data derived from energy consumption monitoring platforms (ECMPs). The second clustering step aims at grouping similar DELPs by means of the k-means algorithm to extract TELPs. The effectiveness of the proposed clustering method is demonstrated by a comparison with two single-step clustering techniques. Furthermore, a classification and regression tree (CART) algorithm is employed to discover insightful knowledge on TELPs and improve the interpretability of clustering results, namely, to explain the relations between dynamic influencing factors related to electricity consumption and TELPs. The proposed framework is applied to analyze the time-series electricity consumption data of three practical office buildings in Chongqing, and its effectiveness has been confirmed. A potential application of discovered knowledge is presented: early fault detection of anomalous electricity load profiles. The proposed framework can provide building managers with an efficient way to understand the characteristics of building electricity usage patterns and detect anomalies therein.