AI-Driven Optimization of Energy Consumption in Smart Grids

EasyChair Preprint 14992

Abstract

The increasing demand for efficient and sustainable energy consumption has driven the evolution of smart grid technologies. This paper presents an AI-driven framework for optimizing energy consumption within smart grids, focusing on the application of machine learning (ML) models to predict energy demand, optimize distribution, and enhance overall grid efficiency. By leveraging big data analytics and cloud computing, the proposed solution offers a scalable and real-time approach to energy management. A synthetic dataset simulating various grid conditions was used to evaluate the framework, demonstrating significant improvements in energy efficiency, cost savings, and grid reliability. Comparative analysis with existing literature highlights the superior performance of the proposed AI-driven approach in enhancing smart grid operations.

Keyphrases: Artificial Intelligence, Big Data, Cloud Computing, Optimization, Smart Grids, energy consumption, machine learning

@booklet{EasyChair:14992,
  author    = {Ethan Roberts},
  title     = {AI-Driven Optimization of Energy Consumption in Smart Grids},
  howpublished = {EasyChair Preprint 14992},
  year      = {EasyChair, 2024}}