Machine Learning Models To Predict Solar Panel Performance

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Researchers developed machine learning algorithms for predicting performance of photovoltaic modules.

Photovoltaic technology can generate electricity from the limitless supply of solar energy and it is one of the most promising technologies for sustainable energy generation. The output power of a solar cell, however, greatly depends on the environmental conditions. Engineers and powerplant managers need estimation of how much power will be injected by photovoltaic systems to plan optimal generation and maintenance schedules, among other important operational aspects.

For the same, we can seek help from artificial intelligence. There are many algorithms that can estimate the power produced by photovoltaic systems several hours ahead by learning from previous data and analyzing current variables. One of them is adaptive neuro-fuzzy inference system (ANFIS) which is currently widely applied for forecasting the performance of complex renewable energy systems. Researchers are constantly trying to improve these algorithms for better performance.

Researchers from Incheon National University, Korea have developed two new ANFIS-based models to better estimate the power generated by photovoltaic systems ahead of time by up to a full day. The study is published in Renewable and Sustainable Energy Reviews.

The researchers compared their models with other ANFIS-based hybrid algorithms. According to Dr. Jong Wan Hu, who led the research team, the results were very promising. He said, “One of the two models we developed outperformed all the hybrid models tested, and hence showed great potential for predicting the photovoltaic power of solar systems at both short- and long-term horizons.”

Dr. Hu comments, “In terms of software, our models can be turned into applications that accurately estimate photovoltaic system values, leading to enhanced performance and grid operation. In terms of production, our methods can translate into a direct increase in photovoltaic power by helping select variables that can be used in the photovoltaic system’s design.” 


 

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