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End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements.

This paper presents a simulation study to reduce heating and cooling demand of a school building in Seoul Metropolitan Area, Korea. This study aims to cross-compare the impact of passive (e.g. improved thermal performance of envelopes, redesign of the building shape and orientation) vs. active (blind control, lighting control, heat exchanger, and geothermal heat pump) approaches on building energy savings using EnergyPlus simulation. It was found that the energy saving of the original school building design by lighting control is most significant. In addition, the energy saving from the original design to a new improved building design increases by 32%. It is noteworthy that energy saving potentials of each room significantly vary depending on room's thermal characteristics (window-wall-ratio, internal heat generation, ventilation requirement) and orientation. Thus, the energy saving analysis should be introduced at the level of individual space, not at the level of the whole building. Also, simulation studies must be involved for informed rational design. Finally, it was concluded that a priority should be placed on passive building design strategies, such as building orientation as well as control and utilization of solar radiation. Passive building design strategies for enhancing energy efficiency are related to urban, architectural design and engineering issues, and are more advantageous in terms of energy savings than active strategies.