Team Name:
DIS
Team Members:
DIS
We're using data to show how solar energy production could be optimised based of environmental factors such as temperature, irradiation levels and air pollution levels.
As we played around with Rosetta's network map for renewable energy sources, solar panels and their distribution around Sydney caught our collective eye. Solar panels and solar energy have only recently become more cost-effective and powerful, meaning now is a great time to optimise solar panel placements for maximal solar energy production! The greatest obstacle in the way of finding optimal locations are environmental factors. Through hours of research, we've found that the most impactful factors of any given location include irradiation levels, air pollution and average temperature. We aim to find an intersection, and show optimal locations to place solar panels where irradiation levels are highest, air pollution is minimal and the average temperature is low.
Description of Use We are searching for optimal locations for solar energy production pinpointing potential locations with Local Government Areas.
Description of Use In alignment with research on the effectiveness of solar panels, the average temperature of areas may have an impact on solar energy production. We're using this data, along with other data we have collected, to determine which areas are most suited to produce solar energy
Description of Use Since we are considering air pollution levels as one of the key contributing factors in the effectiveness of solar panels, this data is essential to the calculation of optimal locations
Description of Use As we are considering irradiation levels as one of the key factors in the effectiveness of solar panels, this data is paramount to us calculating optimal locations
Description of Use We use this information to show how the existing grid may coincide with our predicted optimal locations for solar panel placement
#Powering Tomorrow, Towards a Sustainable Energy Future
Eligibility: Open to all students and professionals with a background in data science, environmental science, engineering, or related fields.
Go to Challenge | 7 teams have entered this challenge.
