This project addresses the significant contribution of buildings to global direct emissions, particularly through fossil fuel-based heating. It aims to optimize three key components of space conditioning: heat pumps, user behavior, and building thermal characteristics. By integrating clustering, machine learning, and data-driven models, the project seeks to generate comprehensive analytics on these drivers. The ultimate goal is to develop strategies for optimal electrification of space conditioning, reducing emissions, while considering consumers' behaviors and buildings physics.
