The future of energy is intelligent, and at the heart of this transformation is the digital twin. By creating a virtual, real-time replica of an energy asset, an entire power grid, or a single building, we can move beyond reactive management and into a new era of proactive optimization. Digital twins enable energy professionals to monitor performance, simulate scenarios, predict failures, and test changes in a virtual environment before implementing them in the physical world. This powerful technology is essential for enhancing efficiency, ensuring reliability, and accelerating the integration of renewable energy sources.

This program provides a comprehensive and practical deep dive into the principles and application of digital twin technology for smart energy management. Participants will gain a fundamental understanding of how to build, operate, and leverage digital twins to solve real-world energy challenges. The course covers everything from data collection and integration from sensors and IoT devices to the use of predictive analytics and machine learning to optimize asset performance. By focusing on hands-on exercises and real-world case studies, attendees will be equipped to design, implement, and manage digital twin projects that lead to smarter, more resilient, and more sustainable energy systems.

Duration: 10 days

Target Audience:

• Energy Managers and Analysts
• Data Scientists and ML Engineers
• Utility and Grid Operators
• Mechanical and Electrical Engineers
• Building and Facility Managers
• Industrial IoT Specialists
• R&D Professionals in Energy
• Smart Grid Planners
• Technology Consultants
• Asset Managers in Energy

Objectives:

• Master the core principles of digital twin technology in the energy sector.
• Learn to design and build a functional digital twin for an energy asset.
• Understand the key data requirements and integration methods.
• Grasp the complexities of real-time monitoring and data visualization.
• Develop proficiency in using predictive analytics for maintenance and performance.
• Explore best practices for simulating scenarios and testing optimizations.
• Learn about robust approaches to cybersecurity for digital twins.
• Identify the critical legal and regulatory frameworks for data.
• Develop skills in communicating the economic and operational benefits.
• Formulate strategies for a data-driven approach to energy management.

Course Modules:

Module 1: Foundations of Digital Twins

• The concept of a digital twin
• The components of a digital twin
• The value proposition in the energy sector
• The difference between a model, a simulation, and a digital twin
• The role of a digital twin in the energy transition

Module 2: The Digital Twin Lifecycle

• From conception to operation
• Data acquisition and integration
• The role of a clear and focused research question
• Modeling, simulation, and analysis
• Optimization and feedback loop

Module 3: Data and IoT for Digital Twins

• The role of sensors and IoT devices
• Data types: from time-series to geospatial
• The importance of data quality and integrity
• Real-time data streaming and processing
• The importance of a "risk and mitigation" plan

Module 4: Building a Digital Twin

• Designing a digital twin for an energy asset
• The use of a simple scorecard and a dashboard
• Selecting the right software and platform
• The role of a "data story map"
• Creating a digital replica of a wind turbine or solar farm

Module 5: Predictive Analytics & Maintenance

• The difference between reactive, preventive, and predictive maintenance
• The use of machine learning for failure prediction
• Anomaly detection in real-time data
• The importance of a "stakeholder analysis"
• Optimizing maintenance schedules to minimize downtime

Module 6: Optimization and Performance Management

• Using a digital twin for real-time optimization
• Simulating the impact of operational changes
• The role of a program's theory of change
• Optimizing energy consumption in a building
• Maximizing the output of a power plant

Module 7: Digital Twins for Grid Management

• Creating a digital twin of an entire power grid
• Simulating the impact of new energy sources
• The role of a clear and compelling KPI
• The use of digital twins for grid resilience
• Managing a decentralized grid with DERs

Module 8: The Role of AI and ML

• The integration of AI into a digital twin
• Using ML for forecasting and demand prediction
• Reinforcement learning for system control
• The importance of a "risk and mitigation" plan
• AI-driven insights from digital twin data

Module 9: Cybersecurity for Digital Twins

• The unique security vulnerabilities of digital twins
• Protecting the data stream from sensors
• The importance of a "data story map"
• Securing the digital twin platform
• Best practices for a secure digital ecosystem

Module 10: Digital Twins for Green Buildings

• The role of a digital twin in green building design
• Simulating energy performance and daylighting
• Optimizing HVAC systems for efficiency
• The importance of a simple scorecard and a dashboard
• Managing a building's carbon footprint

Module 11: Case Studies

• Case study: A digital twin for a wind farm
• Case study: A digital twin for an oil and gas platform
• Case study: A digital twin for a smart city energy system
• Lessons learned from real-world projects
• The future of digital twins in the energy sector

Module 12: Business Models & ROI

• The economic benefits of a digital twin
• Calculating the return on investment (ROI)
• The role of a "clear and consistent reporting style"
• Different business models for implementation
• Communicating the value proposition to stakeholders

Module 13: Project Management

• The lifecycle of a digital twin project
• The role of a "stakeholder analysis"
• Managing a team of experts
• Navigating data governance and security
• Best practices for project execution

Module 14: Data Governance and Ethics

• The importance of data privacy
• The role of a clear and focused research question
• The ethics of using AI for optimization
• Regulatory compliance
• Data ownership and licensing

Module 15: Software and Tools

• Overview of common digital twin platforms
• The use of Python and key libraries
• The importance of a clear and focused research question
• Data visualization tools
• Best practices for code management

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.