A Dynamic Approach to Building Energy Evaluation under Climate Change.
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| Title: | A Dynamic Approach to Building Energy Evaluation under Climate Change. |
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| Authors: | AHN, SOOHYUN1, KANG, HYOMIN1, MOON, WOOSOK1 woosok.moon@gmail.com |
| Source: | Journal of Applied Meteorology & Climatology. Apr2026, Vol. 65 Issue 4, p1-16. 16p. |
| Subjects: | Dynamic simulation, Global warming, Heating load, Energy consumption, Climate change |
| Geographic Terms: | South Korea |
| Abstract: | Climate change has become a critical driver of energy policy and building performance assessment worldwide. In response to rising greenhouse gas emissions and increasing energy demand, the European Union introduced the Energy Performance Certificate (EPC) system in 2002 to standardize and promote energy efficiency in buildings. However, conventional EPC assessments are still based on static weather inputs such as Typical Meteorological Year (TMY) data, which overlook the growing influence of climate variability and long-term warming trends. This study proposes a dynamic simulation framework that incorporates both global warming and interannual climate fluctuations into building energy evaluations. Using 45 years of ERA5 reanalysis data (1979–2023) and EnergyPlus simulations of a standardized residential building in Korea, we quantify how external climate dynamics affect heating and cooling demand. Results reveal that short-term variability often exceeds the energy impacts of four decades of warming, with spatial differences influenced by topography and ENSO phase. These findings highlight the limitations of current EPC methodologies and underscore the need to integrate both gradual and dynamic climate influences into future EPC frameworks to ensure more resilient energy planning. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Meteorology & Climatology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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