I never thought about how old my apartment building is until this summer. I thought about machine learning, data analytics, and statistical thermodynamics, but not about the year a building was built.
For anyone living in California, there is about 50% chance that you are living in a building that is more than 40 years old.
Why does this matter? It is because California’s Building Energy Efficiency Standards first went into effect in 1978, some 40 years ago. That means more than half of the buildings in the state were constructed before the efficiency standards. Without retrofits, these buildings require more energy and cost ratepayers more money to heat up or cool down.
I find myself thinking a lot about the distribution of the age of buildings during my internship at the California Energy Commission (CEC) since I started a month ago. This summer, I am very fortunate to have the opportunity to work with a group of brilliant policymakers and engineers in the Office of Commissioner Andrew McAllister and the Efficiency Division at the CEC to use big data analytics to identify energy saving opportunities from existing buildings.
California has been at the forefront of the nation’s energy and climate change policy. In 2015, Governor Brown signed SB 350, a landmark climate change legislation that in part calls for the doubling of energy efficiency savings in California by 2030. New building efficiency standards and advances in technology such as zero net energy (ZNE) buildings have contributed significantly to our energy efficiency efforts. To achieve California’s ambitious climate change goals, however, we will need to look beyond new buildings and tap into the vast opportunities presented by existing buildings.
It only took me a few days on the job for me to realize that addressing the stock of older buildings is no easy task. With a total of 14 million existing buildings in California and limited resources, it will be difficult to retrofit even just a fraction of them. That’s where data analytics and my work comes in – with statewide building-level energy consumption data, we can identify sectors of buildings that are the most in need of help and present the most significant opportunities for deep energy savings. For example, it might make more sense to provide incentives for building owners to improve insulation in Sacramento than in San Francisco, as I have found out first-hand from wearing suit and tie on 100-degree days here in Sacramento. Using this information, policymakers can then develop targeted initiatives and programs that improve energy efficiency in the most cost-effective ways.
I am having a fantastic experience working at the CEC so far. Just as you would expect, my typical day as a data science intern may involve wrangling messy data into a clean, machine-readable format, trying to figure out a computational efficient way to process gigabytes of data, or visualizing the data in 15 different ways. But more than just being a coding monkey, I get to see the regulatory side of the CEC and every day I gain a greater appreciation for the complexity of the policymaking process. As interns, we get to observe first-hand the rulemaking process and see how regulations are made while balancing various stakeholders’ concerns. Being in Sacramento also allows us to watch up close the legislative actions going on in the Capitol – it was fascinating to watch the legislative fights leading up to the passage of cap-and-trade bill and hear different versions of the politics behind the final outcome.
Working at the CEC has broadened my horizons on data analytics and energy efficiency policies. I am very excited to spend the rest of my summer learning more about how I can combine the two to help make an impact on everyday lives.
Read more at the Out West Student Blog »