Data-driven Decision Making for Electricity Management and Demand Response

Justin Pradipta, Edi Leksono

Abstract


This research centers on utilizing data collected from electricity monitoring systems to facilitate data-driven decision making in electricity management and demand response programs. By leveraging the electricity information system's accurate and timely data on electricity usage, peak demand periods, and load profiles, effective demand response strategies can be implemented. The study aims to optimize energy generation and distribution, support informed decision making for energy policy planning, and enhance overall energy efficiency. Through data-driven approaches, this research strives to create a more responsive and sustainable electricity management system.

References


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Huang, Alex Q., et al. ‘The Future Renewable Electric Energy Delivery and Management (FREEDM) System: The Energy Internet’. Proceedings of the IEEE, vol. 99, no. 1, IEEE, 2010, pp. 133–148.


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