AI Innovation Challenge: Innovate a Solution Building an AI Solution to Optimize Distributed Energy Resources in Buildings
  • The Results

Building an AI Solution to Optimize Distributed Energy Resources in Buildings

Challenge Completed!

In this two-month Omdena Challenge, 50 technology changemakers have been building an AI solution to automate building energy simulations and provide insights on high-impact energy investment opportunities. By optimizing the energy system design of buildings, they have been helping 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 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 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 cost savings potential, more projects will be deployed and contribute to a greener energy system. 


The project outcomes

Rebase Energy has been 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, the team has been building a deep vision engine for rooftop solar PV analysis to improve the scalability and automation of the energy simulation platform. The deep vision engine has been pointing 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 has been to develop a production-ready deep vision engine to provide accurate rooftop solar PV analysis. The data sources have consisted of open satellite and LIDAR data for several European cities. The system has been able to be updated as more labeled data has come online from users of the platform.

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

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