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This article investigates the application of machine learning-based probabilistic prediction methodologies to estimate the performance of silicon-based solar cells. The concept of confidence-bound regions is introduced and the advantages of this concept are discussed in detail. The results show that the optical and electrical performance of a photovoltaic device can be accurately estimated using Gaussian processes with accurate knowledge of the uncertainty in the prediction values. It is also shown that cell design parameters can be estimated for a desired performance metric and trained machine learning models can be deployed as a standalone application.more » « less
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The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for realtime segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected.more » « less
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Moving clouds affect the Global Solar Irradiance (GSI) that reaches the surface of the Earth. As a consequence, the amount of resources available to meet the energy demand in a smart grid powered using Photovoltaic (PV) systems depends on the shadows projected by passing clouds. This research introduces an algorithm for tracking clouds to predict Sun occlusion. Using thermal images of clouds, the algorithm is capable of estimating multiple wind velocity fields with different altitudes, velocity magnitudes and directions.more » « less