Donghyun Seo

This study investigates the long-term impact of insulation degradation on building heating energy consumption, with a focus on extruded polystyrene (XPS) insulation. Year-by-year degradation in thermal transmittance was derived from long-term experimental data and applied to prototypical energy models of multifamily apartment buildings and office buildings. Simulations were performed using both Actual Meteorological Year (AMY) and Typical Meteorological Year (TMY) data for six cities representing Korea’s major climate zones. The results showed that insulation degradation led to a significant increase in heating energy consumption from 23.2% to 34.9% in AMY simulations and 23.5% to 36.2% in TMY simulations for multifamily apartment buildings over 15 years. The difference between the AMY and TMY estimates was within 4%, demonstrating the reliability of TMY for long-term performance assessments. Notably, the southern and Jeju zones exhibited higher sensitivity to degradation due to their relaxed insulation standards and lower initial thermal performance. Office buildings were less affected, with increases below 8%, attributed to smaller envelope areas and higher internal heat gains. These findings highlight the need for zone-specific insulation standards and differentiated energy-saving design strategies by building type to ensure long-term energy efficiency.

Division / Sponsor: Advanced Energy Systems Division (AESD) & Solar Energy Division (SED)

To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO₂ density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: (1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and (2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

A double skin façade (DSF) aims at reducing heating and cooling loads by taking advantage of daylight and utilizing heated air for space heating. To take advantage of a DSF a sophisticated design is required. In this paper a DSF building that has overheating problems was selected to verify the causes and propose remedial solutions. The problem was verified by analyzing the measured solar radiation, inside and outside temperature and air velocity. The results of the measurement analysis showed that airflow congestion caused overheating inside the building. A solar chimney channel (SCC) was proposed as a solution to enhance natural ventilation (NV) through the DSF. Simulations using EnergyPlus incorporating an airflow network were used to verify the proposed solutions. Two remedial design options were considered. The first was to extend the height of the solar chimney channel and the second was to increase penetration of solar radiation through the outer skin and the solar absorptivity of the inner wall of the SCC path. The thermal environment inside the DSF and the indoor cooling load of spaces adjacent to the DSF were investigated through simulation. The results showed that natural ventilation of the inside of the DSF would be increased by applying the two options. Especially, in the case of improving the thermal effect at the SCC path, the annual cooling load could be reduced by almost 3% (2.84%) compared with a non-SCC case.