AI-Driven Predictive Maintenance and Fault Detection in Energy Networks
Word Count : 3000
Objectives to cover:
Introduction – Introduces the concept of AI-powered optimization in smart grid energy management.
Background and Motivation – Highlights the need for intelligent energy systems to improve efficiency and sustainability.
Overview of Smart Grid Technology – Describes the architecture and functionality of modern smart grids.
Challenges in Traditional Energy Management Systems – Discusses inefficiencies, scalability issues, and limited adaptability of legacy systems.
Role of Artificial Intelligence in Smart Grids – Explains how AI enables automation, prediction, and decision-making in energy distribution.
System Architecture of the Proposed AI-Based Framework – Outlines the design and components of the AI-driven optimization framework.
Data Acquisition and Preprocessing Techniques – Covers methods for collecting, cleaning, and preparing energy data for analysis.
Machine Learning and Optimization Algorithms Used – Details the AI models and optimization techniques applied for performance enhancement.
Conclusion and Future Scope – Summarizes findings and suggests potential directions for future smart grid advancements.