Artificial Intelligence-Driven Optimization for Smart Grid Energy Management and Load Forecasting
Year: 2025
Type: article
Abstract: The fast transformation of energy infrastructure has led to the adoption of clever grids, which leverage advanced technology for green energy management and cargo forecasting. synthetic Intelligence (AI)-driven optimization plays a important role in improving the performance of smart grids by means of permitting real-time energy distribution, demand forecasting, and fault detection. This examine explores the combination of AI strategies including gadget gaining knowledge of, deep mastering, and reinforcement studying in optimizing smart grid operations. The outcomes highlight how AI-powered forecasting models enhance strength performance, reduce operational fees, and decrease energy losses. moreover, this study discusses the demanding situations and boundaries of AI-driven optimization in smart grids, inclusive of information privateness concerns and computational complexities. The look at concludes by way of emphasizing the ability of AI in revolutionizing smart grid power management and presenting destiny guidelines for studies on this area. Key Words: Smart Grid, Artificial Intelligence, Load Forecasting, Energy Management, Machine Learning
DOI: 10.55041/ijsrem43955
Authors: Dr Anand Gondesi, Seerapu Varalakshmi, Ch Ravi Kumar
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409238059