Global Energy Supply – Diego Moya

Diego Moya 2022

Global Energy Supply

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide.

Highlights
*A novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating.
*Unsupervised Machine Learning (UML)-based approach assesses large raster datasets of 165 countries, covering 99.6% of worldwide energy users.
*The method captures the complexity and heterogeneity of the residential sector.
*A sustainable scenario for the long-term transition of space heating technology is assessed.

URL: https://doi.org/10.1109/BigData52589.2021.9671339

Method: Geospatial big data analytics
Completion: Dec, 2021
Energy Sector: Residential
Collaborators: Sara Giarola, Adam Hawkes

Global Energy Supply

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide.

Highlights
*A novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating.
*Unsupervised Machine Learning (UML)-based approach assesses large raster datasets of 165 countries, covering 99.6% of worldwide energy users.
*The method captures the complexity and heterogeneity of the residential sector.
*A sustainable scenario for the long-term transition of space heating technology is assessed.

URL: https://doi.org/10.1109/BigData52589.2021.9671339

Method: Geospatial big data analytics
Completion: Dec, 2021
Energy Sector: Residential
Collaborators: Sara Giarola, Adam Hawkes

Global Energy Supply

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide.

Highlights
*A novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating.
*Unsupervised Machine Learning (UML)-based approach assesses large raster datasets of 165 countries, covering 99.6% of worldwide energy users.
*The method captures the complexity and heterogeneity of the residential sector.
*A sustainable scenario for the long-term transition of space heating technology is assessed.

URL: https://doi.org/10.1109/BigData52589.2021.9671339

Method: Geospatial big data analytics
Completion: Dec, 2021
Energy Sector: Residential
Collaborators: Sara Giarola, Adam Hawkes

Global Energy Supply

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide.

Highlights
*A novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating.
*Unsupervised Machine Learning (UML)-based approach assesses large raster datasets of 165 countries, covering 99.6% of worldwide energy users.
*The method captures the complexity and heterogeneity of the residential sector.
*A sustainable scenario for the long-term transition of space heating technology is assessed.

URL: https://doi.org/10.1109/BigData52589.2021.9671339

Method: Geospatial big data analytics
Completion: Dec, 2021
Energy Sector: Residential
Collaborators: Sara Giarola, Adam Hawkes

Global Energy Supply

Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide.

Highlights
*A novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating.
*Unsupervised Machine Learning (UML)-based approach assesses large raster datasets of 165 countries, covering 99.6% of worldwide energy users.
*The method captures the complexity and heterogeneity of the residential sector.
*A sustainable scenario for the long-term transition of space heating technology is assessed.

URL: https://doi.org/10.1109/BigData52589.2021.9671339

Method: Geospatial big data analytics
Completion: Dec, 2021
Energy Sector: Residential
Collaborators: Sara Giarola, Adam Hawkes