USI 167: Intelligent Data Harvesting for Multi-Scale Building Stock Classification and Energy Performance Prediction

Residential buildings account for 14%-27% of greenhouse gas (GHG) emissions in the three jurisdictions and cause significant negative impact on the environment. Supported by NSF, Science Foundation Ireland, and the Department for the Economy in Northern Ireland, this joint research aims to reduce residential building energy consumption, its related GHG emissions and environmental impacts across the three jurisdictions. The proposed research will create a novel decision support tool to inform policy makers, planners and other overburdened stakeholders about the most beneficial residential retrofitting solutions at multiple scales (local to national). The novel methodology will lie at the confluence of machine learning, information theory, computational power, and domain specific skills; together these transform diverse public data-sets into actionable information. Empowered by this information, better decisions can guide modern societies towards transformative green solutions for the built environment that leverage sustainable engineering systems and enable the creation of energy-efficient, healthy and comfortable buildings for a nation's citizens. The approach is cognisant of society's need to provide ecological protection while maintaining favorable economic conditions.