ML application developed to enhance solar panel performance

Researchers from India use meteorological data to improve the accuracy of solar power forecasting models


Researchers from India's Thapar Institute of Engineering and Technology have developed a machine learning-based solution that enables the real-time inspection of solar panels.

Research scholar Parveen Bhola and associate professor Saurabh Bhardwaj used past meteorological data to compute performance ratios and degradation rates in solar panels, leading to the development of a new application for clustering-based computation which increases the ability to speed-up inspection processes and prevent further damage.

According to Bhola and Bhardwaj, their proposed method could improve the accuracy of current solar power forecasting models, while real-time estimation and inspection will enable real-time rapid response for maintenance.

"The majority of the techniques available calculate the degradation of photovoltaic (PV) systems by physical inspection onsite," remarked Bhola. "This process is time-consuming, costly and cannot be used for the real-time analysis of degradation. The proposed model estimates the degradation in terms of performance ratio in real time."

The researchers developed a model that estimates solar radiation through a combination of the Hidden Markov Model, used to model randomly changing systems with unobserved or hidden states, and the Generalized Fuzzy Model, which attempts to use imprecise information in its modeling process.

Both models can be used to adapt PV system inspection methods through the use of recognition, classification, clustering and information retrieval.

"As a result of real-time estimation, the preventative action can be taken instantly if the output is not per the expected value," Bhola added. "This information is helpful to fine-tune the solar power forecasting models. So, the output power can be forecasted with increased accuracy."

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