Module #1 Introduction to Renewable Energy Grids Overview of the importance of renewable energy, challenges in integrating renewables into the grid, and the role of AI-driven optimization
Module #2 Basics of Artificial Intelligence (AI) and Machine Learning (ML) Fundamentals of AI and ML, including supervised and unsupervised learning, neural networks, and deep learning
Module #3 AI Applications in Renewable Energy Overview of AI applications in renewable energy, including forecasting, optimization, and control
Module #4 Renewable Energy Sources:Solar and Wind Overview of solar and wind energy, including technology, benefits, and challenges
Module #5 Energy Storage Systems Overview of energy storage systems, including batteries, pumped hydro storage, and compressed air energy storage
Module #6 Power Grid Fundamentals Overview of power grid operations, including transmission, distribution, and consumption
Module #7 AI-Driven Forecasting in Renewable Energy AI techniques for forecasting renewable energy output, including time series analysis and machine learning models
Module #8 Short-Term Load Forecasting AI techniques for short-term load forecasting, including ARIMA, LSTM, and Prophet
Module #9 Long-Term Load Forecasting AI techniques for long-term load forecasting, including econometric models and machine learning algorithms
Module #10 AI-Driven Optimization in Renewable Energy Grids Overview of AI-driven optimization techniques for renewable energy grids, including linear and nonlinear programming
Module #11 Optimization Algorithms for Renewable Energy Grids In-depth exploration of optimization algorithms, including genetic algorithms, particle swarm optimization, and simulated annealing
Module #12 AI-Driven Energy Storage Optimization AI techniques for optimized energy storage operation, including model predictive control and reinforcement learning
Module #13 AI-Driven Demand Response Optimization AI techniques for demand response optimization, including price-based and incentive-based demand response
Module #14 Microgrid Optimization AI techniques for microgrid optimization, including energy management systems and microgrid control
Module #15 Grid Scale Optimization AI techniques for grid scale optimization, including transmission and distribution system optimization
Module #16 Case Studies:AI-Driven Optimization in Renewable Energy Grids Real-world case studies of AI-driven optimization in renewable energy grids, including success stories and challenges
Module #17 Challenges and Limitations of AI-Driven Optimization Discussion of challenges and limitations of AI-driven optimization in renewable energy grids, including data quality and cybersecurity
Module #18 Future Directions in AI-Driven Optimization Overview of future directions in AI-driven optimization for renewable energy grids, including emerging technologies and research areas
Module #19 Hands-on Exercise:AI-Driven Forecasting Hands-on exercise using Python and popular libraries (e.g. TensorFlow, PyTorch) to develop AI-driven forecasting models
Module #20 Hands-on Exercise:AI-Driven Optimization Hands-on exercise using Python and popular libraries (e.g. Gurobi, CPLEX) to develop AI-driven optimization models
Module #21 AI-Driven Optimization in Renewable Energy Policy and Regulation Discussion of AI-driven optimization in renewable energy policy and regulation, including net metering and time-of-use pricing
Module #22 AI-Driven Optimization in Renewable Energy Markets Discussion of AI-driven optimization in renewable energy markets, including day-ahead and real-time markets
Module #23 Ethical Considerations in AI-Driven Optimization Discussion of ethical considerations in AI-driven optimization, including fairness, transparency, and accountability
Module #24 AI-Driven Optimization in Renewable Energy:A Global Perspective Overview of AI-driven optimization in renewable energy grids from a global perspective, including international case studies
Module #25 Course Wrap-Up & Conclusion Planning next steps in AI-Driven Optimization in Renewable Energy Grids career
