Michael Bendok, Sophomore, Management Science & Engineering
Since I joined the COOLER research team in April, I have encountered as many meaningful findings as provoking questions. With the goal of making large, modern buildings more energy-efficient and resilient, COOLER applies a variety of academic tools to derive insightful results. This summer, I have had the opportunity and privilege to learn from amazing mentors about demand response, data science techniques, econometrics, water chillers, and everything in between.
Caitlin McMahon, Maomao Hu, and Jacques de Chalendar have been instrumental in my understanding of building energy use. First, they taught me the complexities of Heating, Ventilation, and Air Conditioning (HVAC) systems and their role in regulating the temperature in commercial buildings. With this understanding and Caitlin’s guidance, I began monitoring a variety of data points–from pressure to fan speed–in the context of COOLER’s experiments which revolve around increasing building temperature setpoints.
How does increasing Havas’ temperature setpoint by 2°F impact chilled water use on the second floor? Why is a single zone in Varian requesting heating when the room is already 85°F? These questions–and hundreds more–became more complex as Caitlin and Maomao introduced me to data science techniques critical to deriving useful findings. What percent of time did zone-level airflow in Wallenberg fall within 10% of their setpoints? How does the distribution of the median airflow values in Wallenberg compare to that of Havas? As I continued to explore questions–and means of answering these questions–I noticed the distinct yet interrelated nature of these questions.
But one question underlies them all–what do these findings mean for the Stanford community? The purpose of increasing the temperature buildings can reach before requesting cooling is to realize the impact it has on energy use. If we can increase the temperature of Stanford buildings by just 2°F on the hottest days of the year to avoid forced outages, what would the impact be on the buildings? This is the form of demand response we aim to replicate through these experiments. And this has incredibly important implications for reducing emissions, building resilience, and energy efficiency.
Eager to apply demand response to buildings beyond those in the test bed, I began working with Ryan Triolo on the economics and statistics aspects of the COOLER project. Through a preliminary survey, we are gathering information on the Stanford population’s willingness to accept demand response. This has implications beyond Stanford as policy makers and the leaders of institutions can use this survey’s findings (and methodology) to evaluate whether they should implement a demand response program given the costs to the people they represent. We are also working on applying optimization to analyze the effect of demand response on central energy facility operations, to see the effect of a demand response program on Stanford’s energy bill.
Thanks to my mentors–Caitlin, Maomao, Jacques, and Ryan–I have been able to contribute to a project that will improve building resilience, reduce carbon emissions, and optimize energy use at Stanford and beyond. I am excited to continue working on COOLER this summer and into the academic year.
