SLED - Sustainable Local Energy Distribution

Sustainable Local Energy Distribution

Challenge

Supporting sustainable infrastructure development

Problem

SLED project is focusing around the issue of inefficient energy usage, which causes the following problems:

• Underutilized energy surplus leading to energy waste and lost revenue opportunities
• High storage costs preventing large volume storage of produced clean energy
• Economic losses revenues may go to large centralized utilities instead of staying within the community

Solution

We would like to use satellite data for forecasting energy levels in specified areas. This will be used for optimal solar panel placement, and monitoring energy on the existing installations (could be potentially applied to other renewable energy sources, such as wind and water). This will allow to efficiently deliver produced energy within local communities. The problem of energy surplus can be solved by distributing energy surplus to charging stations for electric vehicles in the area. 

SLED will help local enhance sustainable energy sales, increasing local revenue and promoting energy self-sufficiency. Transportation companies will benefit from cutting energy costs, and therefore overall transportation costs. Electric vehicle owners will get affordable and sustainable energy. 

Space for International Development & Humanitarian aid

In regions with low or no renewable energy usage governments and local authorities will benefit from our solution by:

• Identifying locations for optimal solar panel placement
• Delivering stable renewable energy to remote communities
• Planned energy distribution based on predictive analysis from satellite data

EU Space Technologies

In our project, we utilize Satellite data from Copernicus for various metrics to reach our goals. Some of these metrics include cloud density, solar radiation, humidity, population density, and energy demand based on road traffic. Data on cloud density and humidity are derived from the Sentinel-2 satellite, enabling us to make informed decisions regarding renewable energy infrastructure and energy management.

Future Prospects

• Applying the model to other renewable energy sources, such as water and wind
• Direct energy supply for electric vessels
• Recognition of roof and private solar panels using multispectral Sentinel data instead of high-resolution data
• Using additional satellite data for prediction enrichment
• Application for electric vehicle owners to plan sustainable routes