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Research data for the article: Is It Fundamental to Examine the Weather Data for a Reliable Building Energy Simulation? A Comparative Study with Different Weather Datasets
This study investigates the sensitivity of building energy simulations to the selection of different typical-year weather datasets generated from different periods of record compared to the average annual and monthly heating demand and peak load of buildings in different climate geo-clusters of Europe. Three weather datasets are employed: 1) a typical-year weather data generated from years before 2000; 2) a typical-year weather data generated from years between 2006 and 2015; 3) a synthetic weather data produced by modifying the typical-year weather dataset with hourly average actual weather data. The simulation results using these weather datasets including the annual and monthly heating demand and peak load are compared with the average energy use and peak load for the years between 2006 and 2015. The results verify that the annual variation in energy demand for using different typical-year weather datasets ranges from -4.2% and 53.3%. The results also indicate a discrepancy between -49.4% to +4.98% for the peak energy load of the buildings. Hence, compared to other studies, we claim that even more dramatic values for over/underestimation of building energy demand and peak load should be expected when employing different typical weather datasets. This study concludes that 1) updating typical-year weather data with actual average dry-bulb temperature may increase the representativeness of the weather data for calculating annual energy demand; 2) typical-year weather data generated from recent years is a better representative for calculating the peak load of the buildings; and 3) with the current computer capacity and the availability of historical weather datasets from various services, utilizing long-term actual weather data is a more cost-effective and accurate approach to represent the real energy demand of the building. The research calls on the standardization organizations to develop new weather data standards that are suitable for different climate zones and account for the effects of climate change on building energy simulation. It also underscores the significance of weather data selection in renovation studies due to its potential economic impact.