Smart Grids Distribution And Stability Using Deep Learning
Year: 2025
Type: conference-paper
Abstract: Smart Grid Distribution and Stability Using Deep LearningFinal Year Project – BSc IT (Data Science) In today’s world, managing energy efficiently is more important than ever. Traditional power grids often struggle with increasing demand and unexpected faults, which can lead to major disruptions. For my final year project, I explored how Deep Learning can help improve the distribution and stability of Smart Grids. I designed a system that uses real-time data to predict faults and detect instability before they become serious issues. Using neural networks and grid data, the model learns to recognize patterns, helping energy providers react faster and make better decisions. The goal is to make electricity systems smarter, safer, and more reliable—a small but meaningful step toward sustainable energy. I’m proud of the work, and I hope it contributes to real-world solutions in energy management.
Authors: LANCINE, KEITA, Bashr, Ismail, Gubran, Khalil
Language: English
Topic: Smart Grid and Power Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893429234
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