The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R² of the estimation model ranged from 0.406 to 0.703, and the maximum error between measured and estimated values was around ±30%, depending on the end use.

Energy consumption in the building sector has been continuously increasing, and energy conservation in this sector has become critical for achieving the national goal of reducing greenhouse gas emissions. In South Korea, information on energy sources (electricity, gas, district heating, etc.) is provided, but detailed energy use information, such as space heating, space cooling, domestic hot water (DHW) and lighting, is insufficient to establish a specific action plan for energy savings. Energy use information by end-use can be acquired through actual measurements in close proximity to actual energy use. This information reflects the effects of complex elements such as building operations and residential characteristics, but it is also insufficient. This study presents statistical data on energy use intensities (EUIs) and greenhouse-gas-emission intensities by end-use, derived by measured data collected from 48 sample office buildings in representative city from May 2017 to April 2018, and compares those with the U.S. commercial building energy consumption survey (CBECS) 2012 report. The average site EUIs by end-use were in the following order: space heating > electric appliances (typical floors) > space cooling > lighting > air movement > DHW > vertical transportation > city water supply. With regard to the average primary EUIs by end-use, the magnitude relationship between electric appliances (typical floors) and space heating was opposite that of the average site EUIs. Vertical transportation and DHW exhibited almost the same average greenhouse-gas-emission intensities as those of the average annual primary EUIs. The average site EUIs in the CBECS 2012 data were slightly different from those in this study: electric appliances, etc. > space heating > air movement > lighting > space cooling > DHW. The number of office buildings monitored in this study increased until 2019 (the number of total samples: 85), and the intensity data by end-use will be brought up to date through continuous measurement.

This study presents a modular diagnostic framework for evaluating thermal degradation in aging building envelopes by integrating infrared thermography, panoramic reconstruction, and deep learning-based semantic segmentation into a unified workflow. The methodology combines image registration, panoramic synthesis, façade component segmentation, and quantitative surface temperature analysis to provide scalable and reproducible diagnostics. By excluding fenestration zones—where infrared measurements are physically unreliable—the framework focuses on opaque wall regions and window surroundings to ensure physically meaningful evaluation. Field validation was conducted on a multi-story office building constructed in 1996. The diagnostic indicators revealed a mean wall surface temperature of 14.3 °C with a standard deviation of 5.6 °C, and a temperature factor ranging from 0.67 to 0.78 under measured conditions. The vulnerable area ratio reached 9.1% for walls, while window areas showed greater vulnerability at 12.74%, with anomalies concentrated at frame–glass interfaces and perimeter seals. These quantitative results confirmed the framework’s ability to detect thermal irregularities and visualize localized anomalies. More importantly, the contribution of this study lies in establishing a systematic and extensible diagnostic pipeline that advances building envelope analysis, supporting large-scale energy audits, retrofit prioritization, and sustainable building management.

Improving window performance is a key strategy for reducing heating energy demand in residential buildings; however, the economic feasibility of upgrading to higher performance grades remains uncertain, particularly for single-detached houses. This study quantitatively evaluates the energy-saving performance and economic feasibility of window performance grade improvements in single-detached houses in South Korea. Heating energy demand was estimated using ECO2-OD (Energy Conservation Optimization Tool for One-zone Dwelling), the national standard simulation tool adopted for the Building Energy Efficiency Certification (BEEC) and Zero Energy Building (ZEB) certification systems. Representative residential prototypes constructed in 1980, 1987, and 2001 were analyzed to reflect differences in envelope performance associated with construction vintage. Window upgrades from the baseline grade to Grades 3, 2, and 1 using polyvinyl chloride (PVC) windows were simulated under consistent building geometry and operating conditions. Heating energy demand reductions were converted into annual energy cost savings using market-based natural gas prices. Economic feasibility was assessed using annual indicator-based metrics—unit cost of energy saving (UCES), payback period (PBP), and return on investment (ROI)—as well as discounted cash flow-based life-cycle metrics, including net present value (NPV) and discounted payback period (DPB). The results show that heating energy demand decreases consistently with improved window performance across all construction years; however, marginal energy savings diminish at higher performance grades, while investment costs increase. Upgrading from the baseline to intermediate performance grades yields the most favorable economic outcomes, particularly for houses constructed in 2001, whereas upgrades to the highest performance grade often fail to achieve economic feasibility when time value is considered. These findings indicate that uniform application of the highest-grade windows may not be economically optimal. Unlike previous studies that mainly focused on thermal performance or case-specific retrofit outcomes, this study compares multiple window performance grades across construction-year-specific single-detached house prototypes under a unified economic evaluation framework. This study highlights the importance of construction year-specific and performance-tiered retrofit strategies and provides quantitative evidence to support cost-effective window retrofit policies for single-detached residential buildings.

To support national carbon neutrality goals, enhancing the thermal insulation of building envelopes has emerged as a crucial strategy in reducing building energy consumption. This study conducted a detailed quantitative analysis of energy performance improvements achieved through enhanced insulation levels in four representative non-residential building types: office, accommodation, educational, and sales facilities. Based on four scenarios—Baseline (2019), Insulation Reinforced, Passive House, and Zero Energy Building (ZEB)—EnergyPlus simulations were performed to calculate end-use energy demand and consumption. The results revealed that office buildings achieved the highest improvement, with up to 34.7% energy reduction, while educational and sales facilities showed moderate and limited improvements, respectively. These findings provide quantitative evidence for prioritizing insulation-based policies and differentiated ZEB strategies tailored to each building type. The proposed RB models and scenario-based methodology offer a robust foundation for establishing future ZEB regulations and performance-based energy policies in South Korea. To ensure clarity, the study explicitly referenced verified data sources and field measurements. The IdealLoadsAirSystem used in the simulation assumes 100% system efficiency; thus, the reported outcomes represent building system loads rather than final energy consumption. The ZEB-level scenario analyzed in this study focuses on envelope and lighting improvements only, not on HVAC system optimization.