The spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals, some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.

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Varying solar radiation on photovoltaic (PV) panels reduces the PV system's output and increases the mismatch losses. Several techniques have been proposed to solve the mismatch problem, for example, the reconfiguration technique of PV panels.Most of the reconfiguration techniques have been proposed for small systems because of the difficulty in applying to large systems as a large number of switches are required for such applications. In this paper, a new technique based on total-cross-tied (TCT) in two reconfigurable stages is proposed. In the first stage, the shading over the PV plant is dispersed by switching between arrays. In the second stage, the shade dispersion from the first stage is increased by optimizing between the columns. The number of switches and sensors were reduced by using genetic algorithm, thus reducing the system cost. To demonstrate the proposed new reconfiguration technique, four shaded cases were simulated and tested using MATLAB/SIMULINK. A comprehensive analysis of power-voltage (P-V) curves and row currents calculation was performed, for TCT configuration and the proposed new reconfiguration technique. Also, a comparative study on performance analysis, energy-saving, and income generation was carried out for each shading case. The comparative study of the four cases shows that the reconfiguration techniques proposed in two stages generate more power under partial shading conditions than TCT. Further research is needed to ensure the practicality of the switches needed to realize this technique.

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