Introduction to AI – History and evolution, Types of intelligence – AI vs. human intelligence, Learning paradigms, Supervised vs Unsupervised learning , Role of AI in electrical engineering , Rule-based systems vs. machine learning - AI programming techniques.
Artificial Neural Network architectures, Knowledge representation and learning process, Deep learning for fault detection, Explainable AI for grid decisions, Case study: Micro grid management, Comparison with conventional programs.
Fuzzy sets and membership functions, Fuzzy inference systems, AI-based maximum power point tracking, Deep learning for renewable forecasting, Case study: Hybrid energy storage, Real-time implementation challenges.
Genetic operators and modeling, Fitness functions and convergence, GA and neural networks for grid topology, Digital twins for power systems, AI-based demand response, Design thinking for smart cities
Applications in load forecasting and economic dispatch, Edge AI for IoT monitoring, Block chain-AI for energy trading, Case study: Responsible AI implementation, Future trends in AI-EE convergence.
Reference Book:
1. Yan Du., "Deep Learning for Power System Applications", Springer, 1st Edition, 2024. (Units: II, V) 2. El-Hawary, M., "Fuzzy Logic in Electrical Engineering", Wiley, 1st Edition, 2023. (Unit: III) 3. Zalzala, A. M. S., "Genetic Algorithms in Engineering Systems", IET, 1st Edition, 2024. (Unit: IV) 4. Poor, H. V., "Explainable AI for Smart Grids", IEEE Press, 1st Edition, 2023. (Units: II, V) 5. Auci, S., "Block chain and AI in Energy Systems", CRC Press, 1st Edition, 2024. (Unit: V)
Text Book:
1. Jang, J. S. R., "AI and Machine Learning for Engineers", Cambridge, 1st Edition, 2024. its: I, II, V) 2. Russell, S., Norvig, P., "Artificial Intelligence: A Modern Approach", Pearson, 4th Edition, 2023. (Units: I, III, IV)
