The energy consumption of existing buildings depends on their physical features, climatic conditions, and business activities, such as operating hours and occupancy characteristics. It is necessary to perform a fair assessment of building energy performance considering the business activities. It has become especially necessary to collect and manage information on business activities in hospitals since hospitals operate continuously throughout the year, treating patients and using various medical equipment. This study aimed to develop a benchmark that considers business activities and to perform building energy performance assessments in hospitals using the developed benchmark. Initially, the necessary data from hospitals for assessing energy performance and developing an energy benchmark were identified. Then, survey items regarding the business activities and energy consumption of buildings were designed, and a survey was conducted at 48 general hospitals. Secondly, multiple linear regression was used to identify and normalize the major business activities affecting energy use and to develop a benchmark for energy performance assessment. Thereafter, the Energy Efficiency Ratio (EER), the result of comparing the actual energy consumption with the benchmark, was used as an index for the energy performance assessment. Thirdly, additional general hospitals were surveyed to validate the benchmark. The EER of the additional surveyed hospitals was calculated with the developed benchmark. The Energy Use Intensity (EUI) and EER of buildings were reviewed, and analysis was performed to identify why some buildings had a similar EUI but a different EER. Finally, a method to improve the benchmark is presented, and the improved benchmark model is compared with the existing model.

The energy consumption of existing buildings is not only affected by their physical features but also by their business activities (e.g., operating hours, number of workers, and climate). Energy Star’s energy efficiency ratio (EER) is a key energy performance indicator that has been used for more than 20 years. This method normalizes operation characteristics by calculating the estimated energy consumption of business activities using regression models and comparing it with the actual energy consumption. However, EER-based assessment is limited by the lack of information regarding the reasons for the evaluation results. This study proposes a balanced method for explaining the reasons underlying energy efficiency levels while maintaining the existing EER assessment system. The method constitutes data collection, EPI derivation, and energy performance assessment, utilizing the parameters of the change-point linear model (CPM) as an additional EPI to provide descriptive information. The results are summarized to provide a checklist guide for retrofitting, and additional energy saving potential for buildings with low and high scores can be identified by comparing the EER scores and CPM parameters. The proposed method shows that it is possible to interpret the energy efficiency assessment results by comparing CPM parameters, while maintaining the EER score.

Understanding and benchmarking building energy use across urban environments are essential for supporting low-carbon urban strategies and evidence-based energy policy planning. However, conventional simulation-based Urban Building Energy Modeling (UBEM) approaches often require detailed physical information and calibration, limiting their scalability. This study introduces a novel data-driven framework, Intrinsic Degree Hour (IDH), which enables scalable and interpretable profiling of building energy performance using monthly energy consumption and hourly outdoor temperature data. The core of this framework is a regression-based algorithm that estimates building-specific base temperatures for heating and cooling. By optimizing these thresholds, the framework derives IDHs, which characterize each building’s seasonal energy demand pattern. Accordingly, the framework provides diagnostic indicators that represent both the operational characteristics of energy use and the overall energy performance coefficient of the building. The model was validated using sub-metered energy data from 11 commercial buildings. To assess scalability, the framework was applied to 22,615 buildings in Goyang City, South Korea. The results demonstrated the framework’s ability to extract interpretable building energy profiles, distinguish demand-driven consumption patterns, and support urban-scale energy benchmarking. Overall, our framework provides a scalable, data-driven diagnostic approach that complements traditional simulation-based UBEM, particularly in data-limited urban contexts.

Traditional data-driven approaches emphasize input–output correlations and neglect dependencies among inputs, risking missed insights into key drivers of energy performance. Consequently, approaches that transcend correlation-centric analysis are warranted. Within this context, causal inference, which accounts for both statistical associations and temporal cause–effect relations, constitutes a promising direction. However, researchers cannot feasibly specify all causal relations relying solely on domain knowledge. Causal discovery is a data-driven methodology for analyzing causal relationships among variables, providing not only measures of association but also information on causal directionality. The authors employ two causal discovery algorithms—PC (Peter-Clark) and FCI (Fast Causal Inference)—on weather data. The discovered causal structures are compared, and two validation approaches are introduced to evaluate their statistical reliability; the authors also build on the identified causal structure to analyze the resulting causal pathways. The results show that both algorithms provide insights into causal relationships among variables, and the proposed validation approaches help establish the statistical reliability of the discovered structures. Moreover, the analysis of causal pathways indicates that causal effects can be identified and estimated with reliability.

Building energy models often include multiple, strongly interdependent weather variables, yet whether these variables provide distinct and interpretable information beyond outdoor air temperature (OAT) remains unclear. This issue is important for weather normalization, benchmarking, and policy-oriented performance evaluation, where model transparency and coefficient stability matter alongside predictive accuracy. This study applies causal discovery using the Peter–Clark (PC) algorithm to monthly heating-season weather and energy data from four childcare facilities in Seoul, South Korea. A synthetic negative-control case, in which energy use was generated independently of weather, was also included. A synthetic negative-control case, in which energy use was independent of weather, was included to check whether the method avoids spurious weather–energy links; no such links were identified. Graph-based total-effect estimates were compared with coefficients from simple and multiple linear regression models. Across the four real buildings, OAT was the only weather variable with a direct edge to heating energy use in all estimated graphs. Relative humidity, vapor pressure, evaporation, and global horizontal irradiance were connected to heating energy use mainly through pathways involving OAT. Because the estimated graphs contained only a direct OAT-to-energy path, the total-effect estimate of OAT coincided with its simple-regression coefficient. By contrast, jointly entering the weather variables into multiple regression produced sign reversals for several variables and substantial changes in coefficient magnitude, whereas the identifiable graph-based total-effect estimates remained directionally consistent and physically interpretable across the four real buildings. These findings support the use of an OAT-centered parsimonious model as a transparent baseline for the operationally homogeneous buildings examined. Additional weather variables may be warranted when they provide reproducible predictive gains or represent building-specific physical mechanisms.