The energy sector, with its vast and distributed infrastructure, has traditionally relied on manual, time-consuming, and often hazardous inspection methods. This approach is not only inefficient but also fails to capture the nuanced, real-time data needed for proactive management. Computer vision has emerged as a game-changing technology, transforming how companies monitor assets, ensure safety, and optimize operations. It enables the use of drones and fixed cameras to automatically detect anomalies, predict equipment failures, and analyze performance with an unprecedented level of accuracy and speed. This course is designed to equip energy professionals with the skills to leverage these powerful tools for enhanced safety, improved efficiency, and greater sustainability.

This program goes beyond theoretical concepts to provide a practical, hands-on roadmap for implementing computer vision across the entire energy value chain. Participants will learn how to set up intelligent monitoring systems for power grids, analyze thermal imagery of solar farms, and use visual data to predict maintenance needs for wind turbines and oil pipelines. By focusing on real-world case studies and cutting-edge tools, the course prepares professionals to drive the digital transformation of their organizations, reduce operational costs, and build more resilient and responsive energy systems that are prepared for the challenges of tomorrow.

Duration: 10 days

Target Audience:

• Energy Sector Engineers
• Data Scientists and Analysts
• Oil and Gas Professionals
• Renewable Energy Managers
• Asset and Infrastructure Inspectors
• Utility Grid Operators
• Project Managers
• Health and Safety Officers
• Technology Strategists
• Research and Development Teams

Objectives:

• Understand the core principles of computer vision and its relevance to energy.
• Master the process of designing a computer vision-based monitoring system.
• Learn how to select and measure key indicators of infrastructure health.
• Develop a data collection and analysis plan for visual data.
• Integrate ethical considerations and safety protocols for autonomous systems.
• Build a more participatory and inclusive approach to technology adoption.
• Communicate findings to different audiences while managing risk.
• Ensure ethical considerations and safety protocols in data collection.
• Foster a culture of continuous learning and adaptive management.
• Apply computer vision techniques to a wide range of energy sub-sectors.

Course Modules:

Module 1: Introduction to Computer Vision in Energy

• The limitations of traditional inspection methods
• Defining computer vision and its purpose
• The business case for a vision-based approach
• An overview of the computer vision workflow
• The difference between an output and a systemic outcome

Module 2: Foundational Concepts for Computer Vision

• The importance of a clear and focused research question
• Understanding the context and its impact on the project
• The role of a program's theory of change
• The importance of a clear and testable hypothesis
• An overview of the data-to-dashboard workflow

Module 3: Designing a Vision-Based Framework

• The importance of a clear and focused framework
• The role of a "digital" framework
• Integrating a gender and social inclusion analysis
• The importance of a "risk and mitigation" plan
• Case studies on effective framework design

Module 4: Key Performance Indicators (KPIs)

• The importance of a clear and compelling KPI
• The difference between an output, an outcome, and an impact
• The use of a simple scorecard and a dashboard
• Practical labs on a basic performance measurement tool
• The importance of a clear and consistent reporting style

Module 5: Data Collection and Security

• The importance of a clear and secure data collection protocol
• The use of a simple survey and an interview
• The role of a data management system (e.g., Salesforce, SAP)
• The importance of a clear and consistent reporting style
• Protocols for handling sensitive and confidential data

Module 6: Data Analysis and Interpretation

• The importance of a clear data analysis plan
• Using simple statistical analysis for quantitative data
• The role of qualitative data analysis methods
• Interpreting findings from a visual data perspective
• The importance of data triangulation

Module 7: The Art of Storytelling with Data

• The difference between a simple visual and a data story
• The importance of a clear and compelling narrative
• Using dashboards and visualizations to communicate insights
• The role of a "data story map"
• Practical labs on building a data story

Module 8: Ethical Considerations and Safety

• The importance of a "do no harm" approach
• Ensuring the safety and privacy of participants
• The role of informed consent in a crisis
• The importance of a community-led ethical review process
• Protocols for handling sensitive and potentially harmful data

Module 9: M&E for a Diverse Audience

• The importance of knowing your audience
• The role of a "stakeholder analysis"
• Designing reports and dashboards for non-technical audiences
• The importance of accessibility and inclusivity
• Case studies on communicating with different audiences

Module 10: Mainstreaming M&E

• How to integrate M&E into the project cycle
• The importance of a phased implementation strategy
• The role of M&E in the project cycle
• Building a culture of adaptive management
• Case studies on successful integration

Module 11: M&E for a Diverse Sector

• M&E for a technology project
• M&E for a social enterprise
• M&E for a humanitarian project
• M&E for a climate action project
• M&E for a governance project

Module 12: The Role of the M&E Professional

• Shifting from a technician to a facilitator
• The skills required for an M&E professional
• Managing power dynamics and group conflicts
• The importance of a non-judgmental and empathetic approach
• The ethical responsibilities of the M&E professional

Module 13: CBM and Computer Vision

• The potential of community-based monitoring with a focus on computer vision
• Training community members as monitors
• The importance of a participatory approach
• The role of a feedback mechanism for continuous learning
• The long-term benefits of a community-led system

Module 14: Practical Application and Simulation

• A hands-on simulation of a real-world project
• Participants work in teams to design an M&E framework
• Troubleshooting common challenges in data collection
• Analyzing and interpreting a set of data
• Peer review and feedback sessions on framework design

Module 15: The Future of M&E

• The role of AI and machine learning in automated analysis
• The potential of blockchain for data integrity
• The use of new data sources (e.g., satellite imagery)
• The rise of complexity-aware M&E
• The long-term implications for the sector

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.