This paper develops the contemporary role of real-time analytics combining IOT-enabled smart grid systems in order to improve energy efficiency, strengthen grid dependability and privacy sustainability. With help of the set of sensors data analyzed in real-time coming from the smart grid environment, the study will be focused on the energy consumption patterns, demand response efficiency, decision-making ability of predictive maintenance and security threats' detection rate. The findings emphasize a pattern of energy usage which reflects the current scenario as well as the requirements to develop demand response initiatives that are effective in stabilizing energy excesses during peak demand and encourage energy economizing. Firstly, the implementations of predictive maintenance confirm that real-time data analytics effectively sets proactive maintenance appointments thereby, scheduling minimal on-and-offs leading to extended life-span of the equipment. Also, the ripple effect of the recognized cybersecurity threats emphasizes the need for an ongoing monitoring and a substitute of the strong-security system for smart grid infrastructure. This study, therefore, spotlights the importance of current data analytics in headway of smart grid systems' energy management and sustainability solutions, thus urgently calls for new and improved technologies advancement in smart grids and mitigating emerging energy problems.
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Kanimozhi et al. (2024) studied this question.
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