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• Energy signature-based clustering method is proposed for urban building energy analytics. • Combination of energy signature and symbolic transformation is proposed for clustering. • Case study is conducted to analyze energy usage patterns under the COVID-19 pandemic. • Open data problems and energy pattern findings are discussed for a target city. Under the necessity for urban energy savings and the importance of energy demand changes, this paper proposes energy analytics of urban buildings using a novel clustering method with open data. In the proposed method, the concept of energy signatures is introduced, and the signatures are defined as the representative symbols in the symbolic hierarchical clustering. This method can advance the existing clustering method or symbolic aggregate approximation (SAX) with limited energy usage patterns by introducing energy signatures with various pieces of energy information into the symbolic transformation. The proposed method can scientifically support tracking building energy usage and patterns, evaluating the existing concepts (such as green retrofitting and zero energy buildings) and advanced technologies, and the decision-making process for new policies under the global carbon neutrality scenarios. In a case study applied to a city using open energy data (in South Korea), the proposed method determined five representative clusters/areas, revealing the open data quality problems (anomaly and missing data), energy usage changes (e.g., energy usage polarization) caused by COVID-19, and the necessity of classifying building types in terms of energy usage patterns. Specifically, the cluster for decreasing energy patterns accounted for approximately 37%, and the increasing patterns accounted for 25%. Educational buildings accounted for 70% of the decreasing patterns, and technical and medical research facilities accounted for 76% of the increasing patterns under the COVID-19. Approximately 93% of missing data was found in the residential buildings. Anomaly data accounted for 10.9% in the total data. [ABSTRACT FROM AUTHOR]

Buildings are considered the enormous source of untapped energy efficiency potential in the global carbon neutrality. It is necessary to ensure that buildings are energy-efficient using operational pattern analytics and diagnostics. Therefore, this study proposes a novel symbolic hierarchical clustering method (named HOS-SAX) to evaluate the building system operation, efficiency, and energy usage patterns. The proposed HOS-SAX method is intended to enhance the existing methods that focus only on the energy usage characteristics and thus offer limited insights on the building system and operational efficiency. The proposed method consists of: (1) Holistic Operational Signature (HOS) and (2) HOS-based symbolic aggregate approximation (SAX) analyses. A HOS analysis is conducted to derive the representative operational signatures for building operation and efficiency using system-, building-, and weather-level data. Then, SAX is performed with the operational signatures derived from the HOS to cluster the building operation patterns. In a case study for a district heating substation serving residential buildings, the HOS-SAX cluster analysis showed 15 sections in the cluster map that visualize the: (1) energy usage, (2) design efficiency, and (3) control efficiency. The cluster map revealed that the sections that operated inefficiently account for approximately 71% of the entire operation period. Moreover, it is expected that the supply temperature of 0.87 °C can be reduced in the most inefficient sections. • Operational signature-based symbolic aggregation approximation (SAX) method is proposed. • Holistic Operational Signatures (HOS) are presented for a district heating substation. • Severe inefficient sections derived from the HOS-SAX map accounted for approximately 71%. • HOS-SAX cluster analysis can evaluate the building system's operation and efficiency. [ABSTRACT FROM AUTHOR]