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.

The building sector accounts for approximately 30% of global energy use. The demand for energy-efficient, high-performance buildings is increasing given the increasing awareness of the climate crisis. The building envelope greatly influences overall building energy performance. Considering the broad shift from passive to adaptive systems, smart window technologies are attracting attention. Despite their potential, few scholars have examined occupant comfort in spaces with smart windows. This gap is addressed herein by comparatively analyzing occupants’ responses to thermal and visual environments in a room with a smart window (RoomSW) and a room with a conventional window (RoomCW) in a residential building in winter. The smart window is operated via a glare-prevention tint control strategy. The results reveal that under thermal conditions comparable to those in an actual dwelling, wintertime smart window tinting for glare prevention does not decrease occupants’ thermal sensation or satisfaction. Regarding visual comfort, conditions in RoomSW and RoomCW satisfy the minimum illuminance requirement of 200 lx, but glare occurs in RoomCW with a mean New Daylight Glare Index (DGI_N) of 24.1, compared to 9.6 in RoomSW. Questionnaire results indicate greater satisfaction with the luminous environment in RoomSW relative to RoomCW, with scores of +1.4 and +0.2, respectively.

The building sector accounts for approximately 30% of global energy use. The demand for energy-efficient, high-performance buildings is increasing given the increasing awareness of the climate crisis. The building envelope greatly influences overall building energy performance. Considering the broad shift from passive to adaptive systems, smart window technologies are attracting attention. Despite their potential, few scholars have examined occupant comfort in spaces with smart windows. This gap is addressed herein by comparatively analyzing occupants’ responses to thermal and visual environments in a room with a smart window (RoomSW) and a room with a conventional window (RoomCW) in a residential building in winter. The smart window is operated via a glare-prevention tint control strategy, whereby the tint level is adjusted stepwise when glare occurs. The results reveal that under thermal conditions comparable to those in an actual dwelling, winter-time smart window tinting for glare prevention does not decrease occupants’ thermal sensation or satisfaction. Regarding visual comfort, the conditions in both the RoomSW and RoomCW satisfy the minimum indoor illuminance requirements, but glare occurs in the RoomCW. The questionnaire results indicate greater satisfaction with the luminous environment in the RoomSW relative to the RoomCW. This difference is statistically significant (p < 0.05).