My talk will cover the use of large data sets as a byproduct of human activity and how they can be used to help us characterize energy demand to plan for the better usage of our resources. I present a complex system approach to extract meaningful parameters that allow us to complete sparse data into interpretable models of demand on the built and the natural environment.  

In the first part, I will address the congestion of roads. Billions of spatiotemporal call detail records (CDRs) collected from mobile devices create new opportunities to quantify demand and design strategies for transportation and infrastructure planning. In the second part, I will address strategies of retrofitting the urban building stock. Informed by utility bills and the energy plus software, I present a ranking algorithm based on the inferred heat loss rate of buildings. We propose a path for energy savings, prioritizing retrofits at city scale. I will end my talk presenting my research vision on planning sustainable cities leveraging on scientific understanding, digital footprints of human communication activities and technology adoption.