Renewable Energy Forecasting with Machine Learning Training Course

Module 1: Introduction to Renewable Energy Forecasting

• The role of forecasting in the transition to carbon-neutral power grids
• Taxonomy of forecasting horizons: Intra-hour, day-ahead, and seasonal
• Overview of the physical variables affecting solar, wind, and hydro output
• Economic impacts of forecasting errors: Penalties, balancing, and curtailment
• Industry standards and KPIs: Understanding MAE, RMSE, and Skill Scores
• Practical session: Analyzing the correlation between weather variables and power output in a sample dataset

Module 2: Data Engineering for Energy and Weather Data

• Sourcing and handling SCADA data, satellite imagery, and weather station feeds
• Techniques for cleaning time-series data: Handling gaps, outliers, and sensor drift
• Feature engineering: Extracting diurnal and seasonal patterns from timestamps
• Normalizing and scaling multi-modal data for algorithmic stability
• Addressing data resolution differences: Downscaling NWP data for site-specific use
• Practical session: Building a Python data pipeline to merge SCADA logs with historical weather data

Module 3: Statistical Foundations and Time Series Analysis

• Stationary vs. non-stationary data in renewable energy sequences
• Implementing Autoregressive (AR) and Moving Average (MA) components
• Seasonal Decomposition of Time Series (STL) to isolate trends and cycles
• Understanding the Persistence Model as a baseline for forecasting performance
• Partial Autocorrelation (PACF) for determining model lag requirements
• Practical session: Developing a SARIMA baseline model for hourly solar irradiance prediction

Module 4: Machine Learning for Solar Power Forecasting

• Physical factors: Cloud cover, aerosol optical depth, and panel temperature
• Using Random Forests and Gradient Boosting for non-linear irradiance modeling
• Handling "Clear Sky" models and calculating deviation for better accuracy
• Feature selection for solar: Azimuth, elevation, and relative humidity
• Short-term "Nowcasting" using sky imagers and computer vision
• Practical session: Building a XGBoost model to predict solar plant output based on cloud cover data

Module 5: Machine Learning for Wind Power Forecasting

• Wind physics: Power curves, hub height, and air density adjustments
• Modeling wind speed vs. wind power: Handling the non-linear cubic relationship
• Addressing directional bias: Vector-based wind modeling and spatial correlations
• Managing extreme events: Ramp detection (sudden increases/decreases in wind)
• Support Vector Regression (SVR) applications for wind turbine performance
• Practical session: Developing a wind power curve model to identify turbine underperformance

Module 6: Advanced Deep Learning for Energy Sequences

• Introduction to Recurrent Neural Networks (RNNs) for sequential energy data
• Long Short-Term Memory (LSTM) networks for capturing long-range dependencies
• Gated Recurrent Units (GRUs) for efficient time-series training
• Convolutional Neural Networks (CNNs) for spatial weather pattern recognition
• Attention mechanisms and Transformers for global energy market forecasting
• Practical session: Implementing an LSTM network to forecast day-ahead wind power production

Module 7: Numerical Weather Prediction (NWP) Integration

• Fundamentals of NWP models (GFS, ECMWF) and their role in energy ML
• Bias correction techniques for NWP outputs using machine learning
• Multi-model ensembles: Combining different weather forecasts for better stability
• Spatial-temporal kriging for interpolating weather data across a wind farm
• Real-time API integration for live weather-to-power forecasting
• Practical session: Using a Random Forest to bias-correct an NWP wind speed forecast for a specific site

Module 8: Probabilistic Forecasting and Uncertainty Quantification

• Why point forecasts are insufficient for risk-based grid management
• Implementing Quantile Regression to generate prediction intervals
• Kernel Density Estimation (KDE) for modeling the probability of power output
• Assessing reliability and sharpness: Using the Pinball Loss function
• Generating "Scenario Ensembles" for stochastic power system optimization
• Practical session: Generating a 95% confidence interval for a solar power forecast using Quantile Regression

Module 9: Electricity Price and Load Forecasting

• The relationship between renewable penetration and electricity price volatility
• Modeling system load: The impact of consumer behavior and temperature
• Forecasting "Net Load": Accounting for behind-the-meter solar production
• Detecting "Negative Pricing" events in high-renewable markets
• Using hybrid models to forecast day-ahead market clearing prices
• Practical session: Building a multi-input model to forecast electricity prices based on load and wind supply

Module 10: Model Deployment and Operational Integration

• Architectures for real-time forecasting: From batch processing to streaming
• Monitoring model drift: Detecting when weather patterns or hardware change
• MLOps for Energy: Versioning models for seasonal shifts (Summer vs. Winter)
• API design for integrating forecasts with Energy Management Systems (EMS)
• Best practices for communicating forecast uncertainty to grid dispatchers
• Practical session: Deploying a forecasting model as a REST API and creating a real-time monitoring dashboard

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Phoenix Training Center admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@phoenixtrainingcenter.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254725012095 / +254724452588