Probabilistic Assessment of Heatwaves and Building Energy Demand under Changing Climate

Abstract

Climate change is expected to increase building cooling demand not only by raising average temperatures, but also by intensifying extreme heat conditions that produce short duration peaks and prolonged periods of elevated cooling use. This thesis investigates these effects for Toronto using an integrated framework that combines future climate projections, building energy simulation, and probabilistic modeling of extremes.

The study begins with a review of the literature on climate change impacts on buildings, future weather datasets, and probabilistic approaches for assessing building energy performance. A bias-corrected future climate ensemble is then used to generate EnergyPlus simulations for a prototype building over the period 2003--2094. From these simulations, annual and hourly cooling demand metrics are derived and analyzed together with heatwave characteristics identified using Environment and Climate Change Canada heat-warning criteria.

The probabilistic component of the thesis applies non-homogeneous Poisson processes, Weibull models, maximum value distributions, and Gumbel models to characterize both climate and building response extremes. These models are used to examine changes in heatwave occurrence, cumulative heat exposure, annual extreme heatwave severity, annual peak cooling load, and cooling demand during heatwave periods.

The results show that future warming leads to more frequent and more severe heatwaves, with upward shifts in cumulative heat exposure and annual heatwave extremes. The building energy analysis shows a corresponding intensification of cooling demand. Annual cooling energy use increases, annual peak cooling loads rise, and cooling demand during heatwaves becomes progressively larger, especially toward the upper tail of the distribution. The analysis also shows that stationary models are generally less suitable than non-stationary formulations for representing these future changes.

The thesis demonstrates that future cooling related building risk cannot be understood adequately using deterministic summaries or stationary assumptions alone. By linking evolving heatwave behavior to changes in simulated building demand within a probabilistic framework, it provides a rigorous basis for assessing climate-driven cooling extremes in buildings.