Researchers in Spain have proposed a new standard for solar cell testing, which they say could enable more accurate determinations of a cell’s annual energy yield. Using machine learning, the method processes data sets consisting of thousands of solar spectra, creating representative examples which can then be used to predict average annual efficiency.
Artificial intelligence has found plenty of time and money saving applications within the solar industry, in the automated production of solar panels and the forecasting of their long-term performance, among other areas.
Now, a team of researchers from the Spanish National Research Council (CSIC) and the Institute of Solar Energy at the Technical University of Madrid have demonstrated a machine learning technique, which they say can reduce a whole year’s worth of solar spectrum data to just a few characteristic spectra. This can then be used to quickly determine the optimal solar cell design to maximize energy production at a given location.
“Using a technique of statistics and artificial intelligence known as clustering,” says CSIC researcher Jerónimo Buencuerpo Fariña, “we have achieved a practical method to take into account all changes in sunlight and obtain in just a few hours an optimal solar panel design for any location.”
The method is described in the paper Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations, published in the journal Nature Communications.
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