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Using Machine Learning to Track Solar Panel Installation

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Published on : Dec 21, 2018

Solar panels have gained traction from multiple longitudes across the world, and a number of units in the residential, industrial, and commercial sector are using them. However, there is no track of the number or range of households and industries that use solar panels in a region. A project named “DeepSolar” now aims to map the solar panels installed across a region through the use of machine learning. Knowledge about the per capita installation of solar panels across a region could help in drawing national policies and programs with regards to renewable energy production.

Fortifying the Renewable Energy Sector

A number of system for tracking solar panel installation across region exist, but none of them is accurate enough to become a basis for national policy-making. Hence, the system in question, developed by a couple of engineers at Stanford is projected to help several regional pockets in enhancing their renewable energy sector. Machine learning has emerged as a key domain that has revolutionised growth across several industries in recent times. The scientists and researchers involved in the project believe that the use of machine learning to detect solar panels is the most recent application of machine learning technologies.

Future Projections

The project has been making rapid advancements ever since it was rolled out, and the system developed by the researchers has been tested multiple times. It is expected that the “DeepSolar” project would create ripples across the world and shall invite further research from various institutions and organisations. It would be interesting to see the extent to which this project benefits regions and countries.