Advanced Forecasting Techniques for Renewable Energy Grids
( 30 Modules )
Module #1 Introduction to Renewable Energy Grids Overview of renewable energy sources, grid integration, and the importance of forecasting
Module #2 Basics of Time Series Analysis Introduction to time series components, autocorrelation, and stationarity
Module #3 Traditional Forecasting Methods Overview of ARIMA, Exponential Smoothing, and other classical forecasting techniques
Module #4 Solar Radiation Forecasting Techniques for forecasting solar radiation, including NASAs POWER dataset and machine learning approaches
Module #5 Wind Power Forecasting Methods for predicting wind speed and direction, including numerical weather prediction and machine learning techniques
Module #6 Hybrid Renewable Energy Forecasting Combining forecasts from multiple renewable energy sources, including solar, wind, and hydro power
Module #7 Machine Learning for Renewable Energy Forecasting Introduction to machine learning concepts and algorithms for renewable energy forecasting
Module #8 Random Forests for Regression Applying random forests to renewable energy forecasting tasks
Module #9 Gradient Boosting for Time Series Forecasting Using gradient boosting machines for renewable energy forecasting
Module #10 Deep Learning for Renewable Energy Forecasting Introduction to Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for renewable energy forecasting
Module #11 Long Short-Term Memory (LSTM) Networks Applying LSTM networks to renewable energy forecasting tasks
Module #12 Convolutional Neural Networks (CNNs) for Image-Based Forecasting Using CNNs to forecast renewable energy output from satellite imagery
Module #13 Ensemble Methods for Renewable Energy Forecasting Combining multiple models for improved forecasting performance
Module #14 Uncertainty Quantification for Renewable Energy Forecasting Methods for estimating uncertainty in renewable energy forecasts, including quantile regression and Bayesian approaches
Module #15 Spatial Forecasting Techniques Methods for forecasting renewable energy output across multiple locations, including spatial autocorrelation and spatial regression
Module #16 Nowcasting for Renewable Energy High-resolution forecasting techniques for short-term renewable energy forecasting
Module #17 Grid Integration and Forecasting The role of forecasting in grid operations, including load forecasting and grid stability
Module #18 Case Studies in Renewable Energy Forecasting Real-world examples of advanced forecasting techniques in action
Module #19 Data Quality and Preprocessing for Renewable Energy Forecasting Best practices for data cleaning, feature engineering, and preprocessing for renewable energy forecasting
Module #20 Advanced Topics in Renewable Energy Forecasting Exploring cutting-edge techniques, including transfer learning and explainability methods
Module #21 Renewable Energy Forecasting Software and Tools Overview of popular software and tools for renewable energy forecasting, including OpenPV and PyPSA
Module #22 Machine Learning for Renewable Energy Forecasting in Python Hands-on experience with popular Python libraries for machine learning and renewable energy forecasting
Module #23 Big Data and Renewable Energy Forecasting Managing and processing large datasets for renewable energy forecasting, including Hadoop and Spark
Module #24 Cloud Computing for Renewable Energy Forecasting Scalable computing solutions for renewable energy forecasting, including AWS and Google Cloud
Module #25 Real-Time Renewable Energy Forecasting Challenges and solutions for real-time forecasting, including edge computing and IoT applications
Module #26 Electricity Market Fundamentals for Renewable Energy Forecasting Understanding the electricity market and the role of forecasting in market operations
Module #27 Economic Value of Renewable Energy Forecasting Quantifying the economic benefits of improved renewable energy forecasting
Module #28 Regulatory Frameworks for Renewable Energy Forecasting Overview of regulatory requirements and standards for renewable energy forecasting
Module #29 Stakeholder Engagement and Communication for Renewable Energy Forecasting Best practices for communicating forecasting results to stakeholders, including policymakers and grid operators
Module #30 Course Wrap-Up & Conclusion Planning next steps in Advanced Forecasting Techniques for Renewable Energy Grids career
