To manage the changing U.S. electricity system and to understand the barriers to greater use of clean energy, it is crucial to know who have installed residential solar panels on their roofs and why.
Stanford University scientists have analysed more than a billion high-resolution satellite images using a machine learning algorithm. The algorithm has identified almost every residential solar power installation in the connecting 48 states.
According to the analysis, total 1.47 million installations were found, which is quite a higher number than the estimate. Scientists then combined the U.S. Census and other data with their solar catalogue so as to recognize the factors that have led to solar power adoption.
The recent advances in machine learning is facilitating scientists to know where all these assets are and generate insights about where the grid is going. The associate professor of civil and environmental engineering, Ram Rajagopal, along with professor of mechanical engineering, Arun Majumdar have been supervising the project at Stanford University.
Who Goes Solar?
The researchers found that household income played an important role. For people earning above $150,000 a year, income was playing a major role in their decisions. Low and medium income households have been found, not showing much interest in getting a residential solar system installed, despite of knowing the fact that living in areas where solar panel installation can be profitable in the long term.
For instance, areas with a relatively high electricity rates and lot of sunshine, utility bill savings would exceed the monthly cost of the equipment. According to the findings, solar installers may develop new financial models to satisfy the unmet demand.
DeepSolar – The Machine Learning Program
The team trained the machine learning program, DeepSolar, to recognize residential solar panels by providing nearly 370,000 images. Every image was labelled as either having or not having a solar panel.
Now, DeepSolar can identify features associated with solar panels, like texture, color and size. It could correctly identify an image as containing solar panels 93% of the time and missed about 10% images that had solar installations. With some novel efficiencies, the machine learning program got the job done in a month.
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