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Electrical Engineering

Enhancing smart grid reliability with advanced load forecasting using deep learning

  • 02-01-2025
  • Original Paper
Published in:
Authors
J. Jasmine
M. Germin Nisha
Rajesh Prasad
Published in
Electrical Engineering | Issue 6/2025

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Abstract

The world's population is projected to reach 9.7 billion by 2050, driving increased demand for energy. Efficient management of energy resources is crucial for supporting economic development. Smart grids, enhanced with digital technologies, facilitate bidirectional communication and improve power system resilience. Load forecasting is critical for anticipating energy demand and optimizing grid stability. This article explores the integration of advanced deep learning techniques and firefly optimization for enhanced load forecasting, addressing the challenges of power loss minimization and grid reliability. By analyzing historical load data and optimizing distributed generation capacities, the study provides insights into long-term power system planning. The research highlights the potential of deep learning models in predicting load demand and managing renewable energy sources, offering a robust framework for enhancing smart grid operations.
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Title
Enhancing smart grid reliability with advanced load forecasting using deep learning
Authors
J. Jasmine
M. Germin Nisha
Rajesh Prasad
Publication date
02-01-2025
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 6/2025
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02946-z
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