The implementation of renewable energy systems creates power grid instability because solar and wind power generation operates intermittently thus requiring sophisticated demand forecasting systems for maintaining grid stability and efficiency. This research examines how artificial intelligence (AI) transforms smart demand forecasting for renewable-powered grids by solving essential problems related to intermittency management and real-time data processing and existing grid infrastructure integration. The analysis demonstrates how AI techniques including machine learning (random forests, gradient boosting) and deep learning (LSTMs, transformers) and hybrid physics-AI models outperform traditional statistical methods in terms of accuracy and adaptability. The research uses California ISO and European smart grids as case studies to show how AI-based forecasting improves demand management and decreases fossil fuel backup usage while supporting sustainability targets. The paper investigates system architecture needs while emphasizing the importance of IoT and edge computing and big data analytics. The research identifies major adoption challenges which include data quality issues and computational expenses before recommending future directions for scalable and resilient energy systems through federated learning and predictive maintenance.
Lingamurthy Pokathota (2025) studied this question.