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This paper describes a method for the explicit reconstruction and visualization of various polarization ellipses from the intensities measured after a rotating polarizer. The state of polarization of a light beam is represented by the variation of the electric field amplitude vector with polar angle in the laboratory coordinate system defined by the experimental set-up. The intensity of the light beam measured after passing through a rotating polarizer gives the estimate of the wave electric field component parallel to the polarizer pass axis averaged over a time period. The formulae for the estimation of orthogonal field component and polar angle, in the polarizer coordinate system have been derived. Both the orthogonally polarized components and the polar angle are functions of ellipse parameters that have been deduced from the intensity measurements. Finally, the resultant electric field and the polar angle are mapped to the laboratory coordinate system and compared with the representation of the polarization ellipse obtained from the Stokes vectors.

Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the datasets obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis.