Course Description
Artificial intelligence is transforming how electrical grids are monitored, controlled, and maintained. As power systems integrate more renewable generation, storage, and distributed resources, AI provides the tools to manage complexity, predict failures, and optimise operations in real time.
This course provides practical knowledge and hands-on understanding of AI applications in electrical grid maintenance, control, and operations — including predictive maintenance, smart grid monitoring, AI-based load forecasting, and fault detection and grid optimisation. It bridges power engineering with modern data-driven techniques, directly relevant to the future of grid operations.
What you will achieve
By the end of this training course, participants will be able to:
- Explain how AI and machine learning apply to electrical grid management
- Describe smart grid architecture and data sources
- Apply AI-based load forecasting techniques
- Implement predictive maintenance for grid assets
- Understand fault detection and grid optimisation using AI
- Explain the integration of renewables and storage using AI tools
- Interpret AI model outputs for grid decision-making
- Recognise the challenges of deploying AI in critical power infrastructure
How the course is delivered
The course combines power systems context with practical explanation of AI and machine learning techniques applied to the grid. Real utility use cases illustrate predictive maintenance, load forecasting, and fault detection.
The programme is designed to be accessible to power engineers without a data science background, focusing on applied understanding rather than deep theory.
Designed for
This training course is ideal for:
- Power system and grid engineers
- Electrical engineers exploring digitalisation
- Utility operations and maintenance personnel
- Engineers working on smart grid and renewable integration
- Technical staff bridging power systems and data
Daily programme
- The evolution to smart grids
- Grid challenges: renewables, storage, distributed resources
- Introduction to AI and machine learning for power
- Grid data sources and infrastructure
- Demand forecasting fundamentals
- AI-based load forecasting techniques
- Data preparation for forecasting
- Evaluating forecast accuracy
- Condition monitoring of grid equipment
- Predictive maintenance models
- Transformer and asset health
- Reducing outages through prediction
- AI-based fault detection and location
- Grid optimisation techniques
- Real-time monitoring and control
- Renewable and storage integration
- Deploying AI in critical infrastructure
- Data quality and model reliability
- Cybersecurity considerations
- Case studies and future outlook
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
