Skip to main content
  • Home
  • Research
  • ...
  • 11
  • Confirmation of Candidature - Candidate : Sagthitharan Karalasingham

Confirmation of Candidature - Candidate : Sagthitharan Karalasingham

Artificial Intelligence frameworks to Map Urban Electricity Demand Footprints with Remote Sensing, Computer Vision and Deep Learning Approaches.

Date: 16 November 2020
Time: 11:30 AM - 1:00 PM
Venue: Online
Contact: For more information, please contact the Graduate Research School.
Save to calendar: Download
Modelling energy consumption in an era of climate change, extreme weather events, urban climate phenomena, climate-dependent energy sources, electrification of transportation and consumerisation of power generation has increasingly become a challenge, especially at granular spatial scales. Research into energy forecasting is at nascent stages of studying the complex interactions between macro-scale climate, urban level climate phenomena, urban morphologies and climate-dependent energy generation which shape energy consumption at an urban scale. To address these challenges this PhD project aims to develop an interpretable and explainable AI framework for the spatial and temporal modelling of urban energy demand footprints in Australia.