Module #1 Introduction to Renewable Energy Predictions Overview of the importance of renewable energy predictions, course objectives, and expected outcomes
Module #2 Renewable Energy Sources:An Overview Introduction to various renewable energy sources, including solar, wind, hydro, and geothermal energy
Module #3 Data-Driven Approach to Renewable Energy Predictions Introduction to data-driven approaches, importance of data quality, and challenges in renewable energy predictions
Module #4 Data Collection and Preprocessing Methods for collecting and preprocessing data for renewable energy predictions, including data cleaning, normalization, and feature engineering
Module #5 Solar Energy Predictions Introduction to solar energy predictions, including data requirements, prediction models, and case studies
Module #6 Wind Energy Predictions Introduction to wind energy predictions, including data requirements, prediction models, and case studies
Module #7 Hydro Energy Predictions Introduction to hydro energy predictions, including data requirements, prediction models, and case studies
Module #8 Geothermal Energy Predictions Introduction to geothermal energy predictions, including data requirements, prediction models, and case studies
Module #9 Machine Learning Fundamentals Introduction to machine learning concepts, including supervised and unsupervised learning, regression, and classification
Module #10 Regression Models for Renewable Energy Predictions Introduction to regression models, including linear regression, decision trees, and random forests, for renewable energy predictions
Module #11 Time Series Forecasting Introduction to time series forecasting, including ARIMA, prophet, and LSTM models, for renewable energy predictions
Module #12 Deep Learning for Renewable Energy Predictions Introduction to deep learning concepts, including CNNs, RNNs, and LSTMs, for renewable energy predictions
Module #13 Feature Engineering for Renewable Energy Predictions Introduction to feature engineering techniques, including data transformation, aggregation, and feature extraction
Module #14 Model Evaluation and Selection Introduction to model evaluation metrics, including MAE, RMSE, and R-squared, and model selection techniques
Module #15 Data Visualization for Renewable Energy Predictions Introduction to data visualization techniques, including plotting, charting, and dashboard creation, for renewable energy predictions
Module #16 Case Studies in Renewable Energy Predictions Real-world case studies in renewable energy predictions, including solar, wind, hydro, and geothermal energy
Module #17 Challenges and Limitations in Renewable Energy Predictions Discussion of challenges and limitations in renewable energy predictions, including data quality, model complexity, and uncertainty
Module #18 Future Directions in Renewable Energy Predictions Discussion of future directions in renewable energy predictions, including the role of AI, IoT, and data analytics
Module #19 Project Development and Implementation Guided project development and implementation, including data collection, model development, and deployment
Module #20 Project Evaluation and Presentation Project evaluation and presentation, including reporting, visualization, and communication of results
Module #21 Renewable Energy Policy and Regulation Overview of renewable energy policy and regulation, including incentives, subsidies, and grid integration
Module #22 Energy Storage and Grid Integration Introduction to energy storage and grid integration, including battery storage, pumped hydro storage, and power transmission
Module #23 Electrical Load Forecasting Introduction to electrical load forecasting, including short-term and long-term load forecasting, and application to renewable energy systems
Module #24 Smart Grids and Renewable Energy Introduction to smart grids and renewable energy, including advanced metering infrastructure, grid management, and demand response
Module #25 Renewable Energy Certificates and Trading Overview of renewable energy certificates and trading, including carbon credits, green tags, and emission trading
Module #26 Cost-Benefit Analysis of Renewable Energy Systems Introduction to cost-benefit analysis of renewable energy systems, including financial modeling, and levelized cost of energy
Module #27 Risk Analysis and Uncertainty in Renewable Energy Predictions Introduction to risk analysis and uncertainty in renewable energy predictions, including sensitivity analysis and Monte Carlo simulations
Module #28 Data-Driven Decision Making in Renewable Energy Introduction to data-driven decision making in renewable energy, including data-driven policy, regulation, and investment
Module #29 Scalability and Replicability of Renewable Energy Predictions Discussion of scalability and replicability of renewable energy predictions, including large-scale renewable energy deployment and technology transfer
Module #30 Course Wrap-Up & Conclusion Planning next steps in Data-Driven Renewable Energy Predictions career
