The electric grid has begun a profound transition from primarily using carbon-intensive energy to instead using carbon-free renewable energy. Data-driven analyses and teachniques are playing a key role in this transition e.g. researchers are actively developing techniques such as machine learning algorithms that leverage anergy data from the grid to improve energy efficiency and facilitate the energy transition. Unfortunately however, much of this research does not focus on equity and fairness. In this talk, I will describe several examples of decarbonization techniques that make inequitable decisions due to underlying biases in energy usage data. I will argue for making equity and fairness a key design goal while designing energy systems of the future and present some initial work towards meeting such goals.
John Wamburu is a PhD candidate working with Prof. Prashant Shenoy in the Laboratory for Advanced Software Systems at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. His research focuses on applying techniques from machine learning, statistical time-series analysis and optimization to derive insight from structured and unstructured sensor, textual and image/video data.
Before joining UMass, he spent two years working at the first IBM Research Lab in Africa based in Nairobi, Kenya.