Conf42: Machine Learning 2022

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Using AI to help Energy Industry to Accelerate the Smart Green Transition

Harika Chebrolu
Senior Software Engineer @ Red Hat

Harika Chebrolu's LinkedIn account


The major challenge is that investors/policy makers/railways/electrical department don’t have the enough information to come up with viable solar energy investments which are sustainable and cost-effective hence, 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 thereby we can reduce global warming. One such method is to install solar panels and harness the energy from the sun.

We will detect rooftops and give understandable rooftops classification thus, accelerate the growth of solar installations in a given area in order to identify the potential of facilities’ solar installation depending on the uncluttered surface area, shading, direction, material and location.

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 so that 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.

Furthermore, today’s techniques are susceptible to noise from varying bottom conditions and climatic conditions, shadowing. And the current ecosystem is providing data about the amount of energy production based on solar panels installed on rooftops.

Here, in the present idea, we will spot optimal locations for solar panels installation on the rooftop depending on shadowing and direction, amount of usage, surroundings, etc.

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