Typical meteorological year (TMY) weather files are used in building energy simulations to represent typical weather conditions for a location. The conventional approach to generating TMY weather files uses a set of universal weighting factors determined based on expert judgment. These universal weighting factors can overlook the distinct weather patterns between different locations and their influence on energy performance. A previous study used machine learning to generate location-specific weighting factors with respect to building energy performance. The objectives of this paper are: (1) To assess the applicability of the previous study across varying Canadian climates; (2) To investigate the feasibility of using standardized climate zone-based weighting factors to reduce the computational time associated with extracting location-based weighting factors while still considering local climate conditions to facilitate wider adoption of the proposed methodology. The investigation is conducted for a medium office building for 18 locations across six Canadian climate zones and generates two weather files, TMYSTATION and TMYCZ, for each location. The TMYSTATION weather files are generated using location-based weighting factors for each location whereas the TMYCZ weather files are generated using climate zone-based weighting factors for each location. The coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) are used to compare the performance of the proposed weather files with conventional weather files using the energy demands obtained through building simulation. The TMYSTATION and TMYCZ weather files outperform the conventional weather files in predicting the long-term energy performance of buildings. Although the TMYSTATION files marginally outperform the TMYCZ weather files in representing the long-term weather data, the convenience of standardized climate zone-based weighting factors allows the approach to be widely deployed.