Diego Moya
Energy Transition Analyst
Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector
Highlights
• A high-resolution spatio-temporal approach for estimating global heat and cooling energy demand.
• K-means clustering for deriving energy density bands of each heat and cooling energy demand.
• Open-access data for spatial energy density bands for 165 countries covering 99.96% global energy users.
• 5% of heat demand is at very high energy densities worldwide, while >50% is at very low density.
Method: Geospatial big data analytics
Completion: May, 2019
Energy Sector: Residential
Collaborators: Julia Sachs, Sara Giarola, Adam Hawkes
URL: https://www.sciencedirect.com/science/article/pii/S0306261919308657.