## 1 Introduction
### Energy Use Increase Due to Climate Change  
The conventional approach of using Typical Meteorological Year (TMY) data to analyze and quantify buildings' energy consumption has been used and discussed for several years. Various investigations indicate that buildings' thermal and energy performances in the long term are not adequately represented with the commonly used TMY. Researchers emphasize the importance of creating future weather data that considers the yearly variations due to the nature of the standard TMY, neglecting the annual variations caused by the weather changes. In the investigation by [PERSON] et al. [41], they modified the TMYs using the Morphing method to calculate the energy demand of buildings in Shanghai, taking into consideration the RCP4.5 from the Intergovernmental Panel on Climate Change (IPCC). [PERSON] et al. presented a methodology to generate TMYs based on satellite data; then, the weather data was validated against 487 European meteorological stations [42]. The authors mention that the TMY concept, conceived more than 30 years ago, could be replaced by using time series with 10 years or more data and that this will result in accurate information and faster results. Their standard deviation indicates that a better approach could be obtained by applying a more extended time series rather than employing regular TMY data. In the research by [PERSON] et al., a machine learning method is presented to improve the weather files required for the energy performance analysis on buildings, taking into account climate change scenarios [43]. The investigation proposes a methodology for general circulation models (GCM) toestimate the hourly future building energy performance. The quantile-quantile method was applied to diminish the data bias to fit the GCMs to a specific geographic location. Afterward, a hybrid classification-regression model was applied to reduce the corrected GCM data. The designed workflow utilizes observed weather data to find similar historical weather data patterns and then use it to generate future weather data sets. Many studies have performed energy and thermal evaluation of buildings to confirm the future use of modified TMVs. [PERSON] et al. calculated the future energy consumption of buildings to increase the cooling requirements between 29% and 31% and a decrease in the heating requirement between 21% and 22% [44]. [PERSON] et al. [45] performed a study to contrast measured weather data from selected weather stations in Perugia to understand the urban heat island (UHI). As a result of this investigation, the authors state that their future work will be centered on creating weather files that consider the impact of the appearance of heatwaves and cold waves, UHI, and other climate change effects. [PERSON] et al. [46] emphasize that accurate weather information is vital to estimate buildings' energy performances, which is currently achieved using TMY files as the TMY3 file, conceived by the Department of Energy (DOE). The TMY3 represents past climate behavior, and it has been found that it does not adequately estimate the economic feasibility and future energy performance. This investigation introduces a novel hybrid modeling methodology that estimates the probability and establishes a machine regression to predict long term performance. [PERSON] et al. [47] performed a large-scale simulation of three office buildings to quantify the impact on the peak electricity demand for 30-year weather data. This study's particular conclusions are that the annual climatic variation affects the peak electricity demand compared to the overall building's energy consumption. Another issue studied is the impact of the electric grid's energy demand over the years [48]. The electricity demand differences were modest at the early projection time; nevertheless, the changes become more substantial in the last 10 years of the analysis. The cooling demand leads to an increase in energy consumption and the temperature in winter and summer. [PERSON] et al.