AI Innovation Challenge Building an AI Solution for Optimization of Distributed Energy Resources
  • The Results

Building an AI Solution for Optimization of Distributed Energy Resources

Challenge Completed!

In this two-month Omdena Challenge, 50 technology changemakers are building an AI solution to automate building energy simulations and provide insights on high-impact energy investment opportunities. Optimizing the energy system design of buildings will help in the transition to decarbonized and distributed energy resources.


The Problem

Buildings are consuming 40% of the world’s energy and are responsible for 30% of the global carbon emissions. In order to reach net-zero emissions goals, the world’s building stocks will rapidly need to transition to sustainable energy. New European regulations will give the economic incentives to push building owners to rapidly find energy efficiency improvements such as solar PV (Photovoltaic) systems and storage installations for their portfolios. The major challenge is that high potential clean energy projects remain unidentified and thus not getting deployed. By making visible the energy and costs savings potential, more projects will be deployed and contribute to a greener energy system. 


The Project Goals

Rebase Energy is developing a platform for energy simulation and optimization of distributed energy resources such as solar PV, batteries, electric vehicles, and heat pumps. In this project, you will build a deep vision engine for rooftop solar PV analysis to improve the scalability and automation of the energy simulation platform. The deep vision engine will point out the following:

  • Total roof area
  • Roof obstacles 
  • Available roof area (=Total roof area-Roof obstacles)
  • Shadows
  • Roof material 


The above data points can be used as input into a detailed building energy simulation. The goal of the project is to develop a production-ready deep vision engine to provide accurate rooftop solar PV analysis. The data sources will consist of open satellite and LIDAR data for several European cities. The system should be able to be updated as more labeled data comes online from users of the platform. 

The platform operates across building portfolios and thereby helps the property owners to identify and prioritize top candidates for solar PV and battery installations in terms of return on investment and carbon emission reduction. 


Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works


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