Globally, buildings account for 30% of end-use energy consumption and 27% of energy sector emissions (including both direct and indirect emissions). However, the building sector lacks the high spatial- and temporal-resolution data necessary for evidence-based decision-making about energy efficiency retrofits and global sustainable development.
Researchers at the Energy Data Analytics Lab are working to fill this gap by using remote sensing data and machine learning techniques to generate actionable estimates of energy consumption and emissions from buildings across the globe.
