Email this article Application of AI for load shifting in energy management
Abstract
This paper examines the use of Artificial Intelligence (AI) for load shifting in energy management systems. Load shifting involves redistributing energy consumption across different time intervals to optimize energy efficiency and demand response. The paper reviews existing literature on AI algorithms and methodologies for load shifting and highlights their benefits, including reduced costs, enhanced grid stability, and decreased environmental impact. It also discusses challenges such as computational complexity and data availability. The paper suggests future research directions, emphasizing hybrid AI techniques, real-time decision-making algorithms, and integration with emerging technologies like IoT and blockchain. The findings contribute to understanding the role of AI in load shifting, benefiting energy managers, policymakers, and researchers working on sustainable energy systems.
References
Almehizia, A. A., Al-Masri, H. M., & Ehsani, M. (2018). Integration of renewable energy sources by load shifting and utilizing value storage. IEEE Transactions on Smart Grid, 10(5), 4974-4984.
El-Baz, W., & Tzscheutschler, P. (2015). Short-term smart learning electrical load prediction algorithm for home energy management systems. Applied Energy, 147, 10-19.
Rahim, S., Khan, S. A., Javaid, N., Shaheen, N., Iqbal, Z., & Rehman, G. (2015, September). Towards multiple knapsack problem approach for home energy management in smart grid. In 2015 18th International Conference on Network-Based Information Systems (pp. 48-52). IEEE.
Scott, J, Grid Edge: Artificial Intelligence for Energy Systems, Presentation delivered at International Energy Agency Workshop on Modernising Energy Efficiency through Digitalisation, Paris, 27 March 2019
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