Typical Meteorological Years (TMY) are widely used in local-scale building simulation to reproduce realistic response to meteorological conditions using historical data. Current TMYs fail to reproduce building stock response to meteorological conditions on larger scale due to temporal incoherence of TMY from multiple stations. This study presents a method to determine a list of 12 “typical months” representative of all meteorological stations of Quebec province. This list of 12 provincial typical months can be used to form a provincial-purposed TMY for each station. This allows more realistic estimation of the peak demand in large scale simulations, without requiring the use of several actual meteorological years.

Considering the impact of energy efficiency on energy resilience is a relatively new application area for building performance simulation analysis. Such an analysis can assess the impact of increased efficiency on occupant exposure to extreme heat and cold. Exposure is based on building performance occurring during a location-specific extreme heat or cold event. It is expressed in terms of thermal resilience metrics that characterize indoor conditions and occupant comfort. This paper expands upon the scope of the foundational exposure analysis to more fully consider the analysis components needed to quantify the benefits of resilience investment. In addition to exposure analysis, the research study investigates hazard risk identification, vulnerability assessment, and mitigation valuation. The effort demonstrates three areas of application: investment decision matrix, net present value analysis, and resilience benefit of current energy code adoption. While the research advances methods to quantify resilience benefits, continued development can help improve the robustness of some analysis components. In an effort to advance analytical methods, the methods applied in the study are being posted on an open source platform to encourage broader development through industry collaboration. This paper summarizes the research project, describes the analysis methods, presents results, and discusses follow-on collaboration opportunities.

Buildings are expected to be resilient toward future climate change and more frequent disrupt events. Therefore, understanding building performance under various future weather conditions and disrupt events is crucial. From this perspective, we developed a data generator tool for power outage events and future typical weather files, which provides both raw and synthetic data for researchers. They can download ready-to-use data with a single click by simply inputting the city name, time span, and CO2 emission scenarios. In addition, we conducted an EnergyPlus case study to compare energy consumption differences and building performance under power outages and future weather files. The results indicate that in an extreme warm weather condition under RCP8.5, ambient air temperature could increase by up to 7°C, which doubled the cooling demand. While the 50th and 90th percentile of history power outage is 5.5 hours and 72 hours respectively and can be used as reference for resilience-based design. The study also identified extreme weather as the dominant factor in power outage events, potentially causing up to 60% uncertainty in energy system management.

At present, engineering design assumptions relating to climate conditions (temperature, humidity, wind, rainfall) are based on statistical analysis of historical weather data. As climate change is now considered to be foreseeable and, to some extent, quantifiable, it must be taken into consideration regarding engineering design assumptions from both a good practice and a legal or liability point of view. While there is consensus that climate is changing, there is no international, industry wide consensus on how these changes will impact engineering design assumptions. As a result, project design teams do not currently have the tools needed, such as quantifiable design data, to address the technical implications or legal risks posed by climate change.

This study focused on producing and validating a methodology to use climate change projections to calculate future ASHRAE design conditions. The methodology produced used the latest CMIP6 Global Circulation Models (GCMs) from the IPCC AR6 report. It has been tested and applied to all global ASHRAE stations. The results show that future temperatures and other design conditions are likely to be more extreme than historical conditions and should be considered to ensure resilience.

Typical meteorological year (TMY) weather files are utilized for the depiction of climatic conditions and the simulation of urban environments. Localizing these files for disadvantaged communities can help incorporate context-specific influences and minimize discrepancies between simulated and actual conditions. A framework for the development of Inclusive Meteorological Year (IMY) files is provided for use in data scarcity scenarios. Disadvantaged neighborhoods exhibited deviations and a higher temperature profile compared to their TMY counterparts. Correlation coefficients between TMY and IMY files lie below 0.75 and kernel density estimation showcased that TMY is better suited for reflect micro-climatic conditions in affluent neighborhoods as opposed to its disadvantaged counterpart.