Abstract:
The smart grid represents a paradigm shift in energy management, aiming to optimize energy storage and distribution while accommodating the growing demand for renewable e...Show MoreMetadata
Abstract:
The smart grid represents a paradigm shift in energy management, aiming to optimize energy storage and distribution while accommodating the growing demand for renewable energy sources. In this paper, we investigate the application of a modified Artificial Neural Network with Long Short-Term Memory (ANN-LSTM) in addressing the multifaceted challenges of the smart grid. Through rigorous experimentation and simulation, the ANN-LSTM is evaluated in four diverse scenarios, including normal operation, fluctuating renewable energy, peak demand, and grid instability. The results showcase the model's exceptional predictive accuracy, low Mean Squared Error (MSE), and rapid response times, outperforming other models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), Decision Tree (DT), and Fuzzy Logic. Our findings underscore the ANN-LSTM's potential to revolutionize energy storage and distribution in the smart grid, ushering in a new era of efficiency, sustainability, and resilience in energy management.
Published in: 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
Date of Conference: 26-28 October 2023
Date Added to IEEE Xplore: 08 November 2023
ISBN Information: