Applications of Artificial Intelligence in (Machine Learning /Deep learning) Smart Grid.
1.Er.M.A.Suresh Babu, Lecturer(Sr.Sc)/Department of Electrical and Electronics Engineering
Thiagarajar Polytechnic College, Salem, Tamilnadu-636005
Email.id: amlasuba1977@gmail.com
2.Er.G.Kavitha , Lecturer/ Department of Electrical and Electronics Engineering
Thiagarajar Polytechnic College, Salem, Tamilnadu-636005
Email.id:sanmugamkavi@gmail.com
3.Er.T.Kanimozhi, Lecturer/ Department of Electrical and Electronics Engineering
Thiagarajar Polytechnic College, Salem, Tamilnadu-636005
Email.id:t.kanimozhis@gmail.com
Abstract
Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), plays a significant role in enhancing the efficiency, reliability, and sustainability of smart grids. One of its key applications is load forecasting and demand response, where Machine Learning models predict electricity demand based on historical consumption patterns, weather conditions, and economic factors - helping in real-time energy optimization. AI also enables renewable energy integration, grid fault detection and maintenance, energy theft detection, voltage and frequency stability control, AI predicts EV charging demand and prevents grid overload, Another breakthrough is automated grid control and self-healing networks, where AI enables self-healing smart grids that detect outages and reroute power automatically. Machine Learning models predict failures and adjust grid operations accordingly, enhancing grid resilience. Overall, AI-driven smart grids improve energy efficiency, reduce operational costs, and ensure a more reliable and sustainable power distribution system.
This paper discusses a comprehensive review of AI-based modeling, an AI-enabled smart grid for demand forecasting that leverages machine learning (ML) and artificial intelligence (AI) techniques to predict electricity demand with high accuracy and efficiency. It integrates advanced data analytics with the grid's operational systems to enable better decision-making, enhance grid management, and improve energy efficiency.
Keywords: Artificial Intelligence ・ Machine Learning ・ Deep Learning ・
