Heating, ventilation, and air-conditioning (HVAC) systems usually have a set-point temperature control feature that uses the indoor dry-bulb temperature to control the indoor environment. However, an incorrect set-point temperature can reduce thermal comfort and result in unnecessary energy consumption. This study focuses on a derivation method for the optimal cooling set-point temperature of an HVAC system used in office buildings, considering the thermal characteristics and daily changes in the weather conditions, to establish a comfortable indoor environment and minimize unnecessary energy consumption. The operative temperature is used in the HVAC system control, and the mean radiant temperature is predicted with 94% accuracy through a multiple regression analysis by applying the indoor thermal environment data and weather information. The regression equation was utilized to create an additional equation to calculate the optimal set-point temperature. The simulation results indicate that the HVAC system control with the new set-point temperatures calculated from the derived equation improves thermal comfort by 38.5% (26%p). This study confirmed that a cooling set-point temperature that considers both the thermal characteristics of a building and weather conditions is effective in enhancing the indoor thermal comfort during summer.

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.

Smart Home Energy Management Systems (SHEMS) are widely promoted as tools for improving residential energy efficiency, yet their impact is often limited by interfaces that insufficiently account for human cognition and behavior. This study addresses this gap by proposing a design framework that combines design-oriented approaches from Cognitive Systems Engineering (CSE) and Human-Computer Interaction (HCI) with behavior-oriented approaches from Human Information Processing (HIP) and Human-Building Interaction (HBI). Through abstraction hierarchy-based decomposition of prior HEMS and HBI studies, five core interface functions are derived, such as Information Delivery, Real-Time Feedback, Personal Motivation, Control Flexibility, and Social Comparison, each representing a distinct functional goal for energy engagement. These functions are systematically cross-mapped with Norman’s Design Principles to construct a function-usability matrix of 30 actionable guidelines. The framework is then embedded within a HIP-HBI-SHEMS loop, forming a layered model that specifies how interface elements should align with cognitive stages, occupant behavior, and system responses. The framework is demonstrated through a structured mapping of representative SHEMS interface types, which reveals functional, usability, and cognitive limitations in current designs. The resulting framework offers a structured, theory-informed, and practically applicable basis for designing SHEMS interfaces that can better support sustained energy-saving behavior in residential buildings.