Module #1 Introduction to Renewable Energy Forecasting Overview of renewable energy sources, importance of forecasting, and applications of AI techniques
Module #2 Fundamentals of Artificial Intelligence Basics of machine learning, deep learning, and neural networks
Module #3 Time Series Analysis and Forecasting Introduction to time series analysis, types of forecasting models, and evaluation metrics
Module #4 Solar Energy Forecasting Solar energy fundamentals, data sources, and forecasting challenges
Module #5 Wind Energy Forecasting Wind energy fundamentals, data sources, and forecasting challenges
Module #6 Hybrid Renewable Energy Systems Overview of hybrid renewable energy systems, benefits, and forecasting requirements
Module #7 Data Preprocessing for Renewable Energy Forecasting Importance of data preprocessing, handling missing values, and feature engineering
Module #8 Machine Learning for Renewable Energy Forecasting Introduction to machine learning models for renewable energy forecasting
Module #9 Linear Regression for Renewable Energy Forecasting Applying linear regression to renewable energy forecasting, advantages, and limitations
Module #10 Decision Trees and Random Forests for Renewable Energy Forecasting Applying decision trees and random forests to renewable energy forecasting, advantages, and limitations
Module #11 Neural Networks for Renewable Energy Forecasting Applying neural networks to renewable energy forecasting, advantages, and limitations
Module #12 Deep Learning for Renewable Energy Forecasting Applying deep learning models (e.g. LSTM, CNN) to renewable energy forecasting, advantages, and limitations
Module #13 Ensemble Methods for Renewable Energy Forecasting Introduction to ensemble methods, bagging, boosting, and stacking
Module #14 Hyperparameter Tuning for Renewable Energy Forecasting Importance of hyperparameter tuning, techniques, and best practices
Module #15 Evaluating Renewable Energy Forecasting Models Metrics for evaluating renewable energy forecasting models, mean absolute error, mean squared error, and more
Module #16 Uncertainty Quantification in Renewable Energy Forecasting Importance of uncertainty quantification, techniques, and applications
Module #17 Big Data Analytics for Renewable Energy Forecasting Handling large datasets, distributed computing, and big data analytics tools
Module #18 Case Study:Solar Energy Forecasting using Machine Learning Real-world example of solar energy forecasting using machine learning
Module #19 Case Study:Wind Energy Forecasting using Deep Learning Real-world example of wind energy forecasting using deep learning
Module #20 Industry Applications and Use Cases Applications of AI techniques in renewable energy forecasting, grid management, and energy trading
Module #21 Challenges and Limitations of AI Techniques in Renewable Energy Forecasting Discussion of challenges, limitations, and future research directions
Module #22 Ethical Considerations in Renewable Energy Forecasting Ethical considerations, fairness, and transparency in renewable energy forecasting
Module #23 Tools and Software for Renewable Energy Forecasting Overview of popular tools and software for renewable energy forecasting, including Python libraries and frameworks
Module #24 Best Practices for Implementing AI Techniques in Renewable Energy Forecasting Best practices for implementing AI techniques, model deployment, and maintenance
Module #25 Course Wrap-Up & Conclusion Planning next steps in AI Techniques for Renewable Energy Forecasting career
