The modern electricity grid is undergoing an unprecedented transformation, driven by the increasing penetration of variable renewable energy sources like wind and solar. This shift from a centralized, predictable power system to a decentralized, dynamic one has created new challenges for maintaining grid stability and reliability. The key to navigating this complexity lies in advanced power system data analytics. By leveraging sophisticated tools and techniques, grid operators and energy planners can transform vast amounts of data—from smart meters and sensors to weather satellites—into precise, actionable forecasts that are essential for optimizing operations, reducing costs, and ensuring a seamless supply of clean energy.

This program provides a comprehensive and practical deep dive into the intersection of data science and power systems. Participants will learn how to build, deploy, and evaluate forecasting models that predict renewable energy output and manage the inherent variability of these sources. The course delves into a variety of machine learning and statistical methods, the use of real-time and historical data, and the critical importance of a sound data governance strategy. By focusing on real-world case studies and hands-on exercises, attendees will be equipped to develop robust forecasting solutions that enhance grid efficiency, minimize energy curtailment, and accelerate the transition to a more resilient, data-driven power system.

Duration: 10 days

Target Audience:

• Data Scientists and Analysts
• Power Systems Engineers
• Grid Operators and Planners
• Energy Market Analysts
• Researchers in Energy and AI
• Utility Professionals
• Renewable Energy Developers
• IT and Technology Professionals
• Sustainability Officers
• Energy Consultants

Objectives:

• Master the foundational principles of power system data analytics and renewable forecasting.
• Learn to collect, clean, and preprocess diverse energy and meteorological datasets.
• Understand the key differences and applications of various forecasting models.
• Grasp the complexities of time-series analysis for energy-related data.
• Develop proficiency in building and evaluating machine learning models for solar and wind forecasts.
• Explore best practices in grid reliability management with variable renewables.
• Learn about robust approaches to leveraging data for predictive maintenance and asset management.
• Identify the critical ethical, legal, and regulatory considerations in data management.
• Develop skills in communicating complex analytical findings to different stakeholders.
• Formulate strategies for a data-driven approach to energy transition.

Course Modules:

Module 1: Foundations of Power System Data

• The smart grid data ecosystem
• Types of data: SCADA, AMI, synchrophasors
• Data volume, velocity, and variety in power systems
• The importance of data quality and validation
• An overview of data sources for renewable forecasting

Module 2: Data Collection and Preprocessing

• Data acquisition from various sources
• Data cleaning and outlier detection
• Handling missing and erroneous data
• Time-series data resampling and aggregation
• Feature engineering for forecasting models

Module 3: Time-Series Analysis

• The nature of time-series data
• Statistical time-series models (e.g., ARIMA, Exponential Smoothing)
• Decomposing time-series data: trend, seasonality, and residuals
• The role of autocorrelation and partial autocorrelation plots
• Selecting the right model for your data

Module 4: Introduction to Renewable Forecasting

• The challenge of forecasting intermittent energy sources
• Short-term, medium-term, and long-term forecasts
• The impact of inaccurate forecasts on grid operations
• The role of weather data and numerical weather prediction (NWP)
• Common metrics for evaluating forecast accuracy

Module 5: Machine Learning for Forecasting

• Regression models for forecasting (Linear Regression, Ridge, Lasso)
• Tree-based models (Decision Trees, Random Forests, Gradient Boosting)
• The role of a clear and focused research question
• Introduction to neural networks for forecasting
• Comparing machine learning models for performance

Module 6: Deep Learning for Forecasting

• The power of deep learning for complex time-series
• Recurrent Neural Networks (RNNs) and LSTM models
• Convolutional Neural Networks (CNNs) for spatial data
• Hybrid models combining deep learning and statistical methods
• Implementing deep learning models in practice

Module 7: Forecasting Wind Energy

• Key factors influencing wind generation (speed, direction, temperature)
• Data sources for wind forecasting (e.g., NWP, satellite data)
• Building a wind power forecast model
• Addressing turbulence and wake effects
• Case studies in wind forecasting

Module 8: Forecasting Solar Energy

• Key factors influencing solar generation (irradiance, cloud cover)
• Data sources for solar forecasting (e.g., satellite imagery, ground sensors)
• Building a solar power forecast model
• The challenge of forecasting solar ramps and eclipses
• Case studies in solar forecasting

Module 9: Model Evaluation and Validation

• Common evaluation metrics (MAE, RMSE, MAPE)
• The importance of cross-validation
• Backtesting and out-of-sample validation
• Interpreting forecast uncertainty and confidence intervals
• Communicating model performance to stakeholders

Module 10: Grid Operations and Applications

• Using forecasts for real-time grid balancing
• Optimizing unit commitment and economic dispatch
• Minimizing energy curtailment
• The role of forecasting in energy trading
• Using forecasts for predictive maintenance of assets

Module 11: Big Data and Cloud Platforms

• The case for big data in the energy sector
• Leveraging cloud platforms (e.g., AWS, Azure)
• Architecting a cloud-based analytics solution
• Scalability and cost management in the cloud
• Introduction to streaming data analytics

Module 12: Data Governance and Privacy

• Establishing data governance frameworks
• Data privacy regulations and compliance
• The importance of data security in the energy sector
• Ensuring data is used for social good
• Legal frameworks for data ownership

Module 13: Project Management for Data Projects

• The project lifecycle for a data initiative
• Agile methodologies in a data context
• Scoping a data project for success
• Managing key stakeholders and expectations
• The role of a clear and consistent reporting style

Module 14: Case Studies and Best Practices

• Case study: A utility's journey to data-driven operations
• Case study: Forecasting for a large-scale wind farm
• Best practices from leading energy companies
• Lessons learned from the field
• The future of data analytics in energy

Module 15: The Future of Renewable Forecasting

• The role of AI and digital twins
• The rise of probabilistic forecasting
• Forecasting for microgrids and distributed energy resources
• The convergence of energy and other sectors
• The long-term vision of a data-driven grid

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.