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Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, and uninterrupted electricity. However, for standalone power systems, demand-side management always remains a challenging task. In this paper, a load scheduling algorithm driven by K-mean clustering and linear integer programming to schedule consumers’ appliances for the upcoming day is proposed. In addition, the basic power to run the necessary appliances is kept available in the system all the time. Furthermore, to assist the consumer in every situation, the battery storage system and the overall system size reduction are also taken into consideration. Consumer input is also used in scheduling the appliances. The proposed method is evaluated on the publicly available real-world dataset; the simulation results demonstrate that the proposed approach performs better, due to which the reliability and continuity of the system are increased.

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A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods.