Smart Grids Distribution And Stability Using Deep Learning

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.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-15
DOI
https://doi.org/10.5281/zenodo.15459623
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Grids Distribution And Stability Using Deep Learning

Zenodo (CERN European Organization for Nuclear Research)
Energy Load and Power Forecasting
article

Smart Grids Distribution And Stability Using Deep Learning

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 57%
Energy Load and Power Forecasting
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