Typical Meteorological Year (TMY) datasets are widely used in building energy analysis to represent long-term climatic conditions with reduced computational effort. However, the selection of the TMY generation method may significantly influence building energy performance indicators, particularly in regions with transitional climate characteristics. In this study, hourly meteorological data covering the period 2020–2024 were used to generate TMY datasets for five representative cities located in the Marmara and Thrace regions of Türkiye. The classical Finkelstein–Schaefer method and weighted variants based on ASHRAE and Jiang approaches were applied to construct different TMY datasets. The resulting datasets were evaluated using heating and cooling degree-day (HDD/CDD), degree-hour (HDH/CDH), and BinData frequency analyses. The results reveal that different TMY generation methods lead to measurable variations in heating and cooling indicators at both annual and hourly scales. These variations directly affect the representation of climatic conditions used in building energy performance assessments. The findings highlight the importance of selecting appropriate TMY generation methods, particularly for energy-efficient and Zero Energy Building-oriented design and analysis studies.
Cihan et al. (Sat,) studied this question.