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Achieving carbon neutrality by 2050 requires evaluating and retrofitting existing buildings. However, despite the numerous studies on energy analytics, they usually focus on energy consumption patterns and motifs rather than encompassing various energy usage characteristics. This study proposes a novel symbolic hierarchical clustering for building energy analytics at the city level. It utilizes change-point model (CPM) parameters to represent building energy usage, performance, occupant behavioral characteristics. The clustering method based on the CPM parameters defines energy performance signatures (EPS) for determining their energy characteristics and as symbolic data transformation. In a case study conducted in Gangwon, South Korea, five different energy performance signatures (EPSs 1–5) showing their unique energy characteristics were determined for commercial buildings. EPS1 to 3 were classified as signatures with good performance (65.5% of all buildings) while EPS4 and 5 were classified as signatures with bad performance (34.5%). Using this EPS symbolic data, an EPS map was visualized and analyzed from various perspectives. For example, buildings that showed a continuous or overall decline in envelope performance over five years were among the oldest buildings (construction completion date closer to 1978; 7.9%). Despite poor envelope performance, buildings with lower energy usage showed a tendency for occupants to delay heating (28.4%). The proposed method can contribute to the data-driven building energy analytics in providing detailed insights into energy usage patterns, building energy performance, and occupant behavioral characteristics at the city level. The effectiveness of open-source energy data for urban building energy analysis would be improved through the proposed method. [Display omitted] • Energy performance signature (EPS)-based clustering is proposed for urban building energy analysis. • A combined data-driven five-parameter change-point model and clustering using symbolically transformed data are proposed. • A case study is conducted to analyze energy usage characteristics of commercial buildings in Gangwon, South Korea. • The EPS map from the proposed method provides information on the change in envelope performance, energy consumption pattern, and occupant behavior. [ABSTRACT FROM AUTHOR]

Retrofitting existing buildings is crucial for significantly reducing energy consumption in the building sector. The continuous monitoring and evaluation of retrofit building energy efficiency is necessary to maintain optimal energy performance. This study proposes a novel method for evaluating building energy performance that combines energy signature analysis and hierarchical clustering. Symbolic data were defined by k-means using differences in gradients and y-intercepts before and after retrofitting extracted from energy signatures. Hierarchical clustering was then performed using the symbolic datasets. This symbolic hierarchical clustering method enhances the utility of open data and facilitates rapid decision-making. Additionally, it allows for a simple assessment of energy performance at the city scale. Through implementing this approach in 49 retrofitted buildings in Gangwon-do, South Korea, five types of symbolic data were identified (Types 0–4). Using hierarchical clustering, these buildings were clustered into six groups (Clusters 1–6). Type 3, representing ideal retrofitting outcomes, was observed in Clusters 2, 3, and 4 (73.47 % of all buildings). Conversely, Type 4 symbols, indicating a rebound effect, were observed in Cluster 1 and 2 (6.12 %). These findings provide meaningful information after retrofitting at the regional level, contributing to effective building management during retrofitting. • Retrofit buildings were evaluated using a symbolic hierarchical clustering method. • Parameters extracted from energy signatures were used to define the symbols. • A case study was conducted to evaluate the outcomes of retrofitting in Gangwon-do. • This method contributes to the utilization of open data. [ABSTRACT FROM AUTHOR]

• 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]