The paper explores the integration of advanced data analytics techniques in enhancing smart grid demand response strategies. With the rapid growth of renewable energy sources and the increased complexity of electrical systems, the management of grid demand has become a critical challenge. This study focuses on the application of sophisticated data analytics tools, including machine learning and predictive modeling, to optimize demand response programs. The analysis delves into the collection, processing, and interpretation of large-scale data from various sources, such as smart meters and sensors, to identify patterns and trends in energy consumption. By leveraging this information, the paper proposes a framework for implementing demand response initiatives that are both efficient and effective. The framework is designed to minimize energy costs while maximizing grid reliability and sustainability. The study concludes with a discussion on the potential challenges and future directions for the implementation of data-driven demand response strategies in smart grids.
Jackson, J. Advanced Data Analytics for Smart Grid Demand Response. Information Sciences and Technological Innovations, 2019, 1, 3. https://doi.org/10.69610/j.isti.20191030
AMA Style
Jackson J. Advanced Data Analytics for Smart Grid Demand Response. Information Sciences and Technological Innovations; 2019, 1(1):3. https://doi.org/10.69610/j.isti.20191030
Chicago/Turabian Style
Jackson, James 2019. "Advanced Data Analytics for Smart Grid Demand Response" Information Sciences and Technological Innovations 1, no.1:3. https://doi.org/10.69610/j.isti.20191030
APA style
Jackson, J. (2019). Advanced Data Analytics for Smart Grid Demand Response. Information Sciences and Technological Innovations, 1(1), 3. https://doi.org/10.69610/j.isti.20191030
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