Integrated Artificial Intelligent AI Modeling and Power Electronics for Real-Time Smart Grid Optimization
TL;DR
The results demonstrate that AI-based optimization significantly improves grid resilience, load balancing, adaptive pricing strategies, and operational efficiency, contributing to the development of scalable, intelligent, and sustainable smart grid systems for future energy management.
Abstract
This study proposes a novel AI-powered smart grid management framework that integrates predictive analytics, machine learning, and adaptive control techniques to optimize energy distribution, minimize transmission losses, and improve overall cost efficiency. The proposed system utilizes a comprehensive dataset comprising energy consumption, energy generation, voltage, current, temperature, wind speed, solar irradiance, battery storage, and dynamic electricity pricing to develop an intelligent decision-making architecture. Advanced machine learning algorithms are employed for energy demand forecasting, power flow optimization, and loss minimization, thereby enhancing grid efficiency, reliability, and renewable energy integration. The results demonstrate that AI-based optimization significantly improves grid resilience, load balancing, adaptive pricing strategies, and operational efficiency, contributing to the development of scalable, intelligent, and sustainable smart grid systems for future energy management.