Superstorm Sandy is merely the most recent high-impact weather event to raise concerns about extreme weather events becoming more frequent or more severe. Previous examples include the western European heatwave of 2003, the Russian heatwave and the Pakistan floods of 2010, and the Texas heatwave of 2011. However, it remains an open question to what extent such events may be “attributed” to human influences such as increasing greenhouse gases. One way to answer this question is to run climate models under two scenarios, one including all the anthropogenic forcing factors (in particular, greenhouse gases) while the other is run only including the natural forcings (e.g. solar fluctuations) or control runs with no forcings at all. Based on the climate model runs, probabilities of the extreme event of interest may be computed under both scenarios, followed by the risk ratio or the “fraction of attributable risk”, which has become popular in the climatology community as a measure of the human influence on extreme events. This talk will discuss statistical approaches to these quantities, including the use of extreme value theory as a method of quantifying the risk of extreme events, and Bayesian hierarchical models for combining the results of different climate models. This is joint work with Xuan Li (UNC) and Michael Wehner (Lawrence Berkeley Lab). Event flyer.
Kristen Cetin is a PhD candidate at the University of Texas at Austin, in the Department of Civil, Architectural and Environmental Engineering, in the Building Energy and Environment Group. She is also a licensed professional engineer and a LEED professional. Her research focuses on the use smart grid-connected technologies to reduce building energy use and peak loads, and assessing their effects on building occupants and the indoor environment.
SMART TECHNOLOGY-ENABLED BUILDING ENERGY AND PEAK LOAD REDUCTION AND THEIR EFFECTS ON OCCUPANTS AND THE INDOOR ENVIRONMENT
Building operations consume approximately 72% of electricity in the United States, and are responsible for over 70% of the peak demand on the electric grid, particularly in warm climates. The increasing deployment of technologies such as smart meters, home energy management systems (HEMS), and smart home-connected sensors and devices and their associated data provide an opportunity for data-driven operation and evaluation of the performance of buildings and their systems. This is particularly important as we face challenges in energy price fluctuations, distributed and renewable energy grid integration, and climate variability. More