Learn how AI is used in energy systems—forecasting demand and generation, predictive maintenance, demand response, and real-time scheduling.
Applied AI for Energy is a live, instructor-led program focused on practical AI use-cases in power and energy systems. You will cover IoT and sensor-driven energy analytics, forecasting and price prediction, optimization and reinforcement learning, and control strategies for demand response and aggregators. The program is case-study driven with a final assessment to validate core concepts and application thinking.
Apply AI techniques to energy data: forecasting, optimization, demand response, pricing mechanisms, and real-time scheduling.
Energy Data Signals
Demand + Generation
Asset Optimization
ANN / DL Models
Control Strategies
Demand Response
Energy Context + AI
Not generic ML—focused on grid, demand, pricing, and control use-cases.
Forecasting Driven
Learn demand, generation, and price prediction foundations used for planning and scheduling.
Decision + Control Thinking
From predictions to actions: incentives, scheduling, and demand response strategies.
How energy systems generate signals and how AI turns them into usable insights.
Design incentives and mechanisms that influence consumption and load shifting.
The best part was understanding how forecasting connects to scheduling and demand response decisions in real systems.
AI methods to detect risk early and optimize asset performance.
Learn the RL mindset for control strategies in energy systems.
Real-time electricity scheduling methods and how forecasts improve DR signals and aggregator services.
Earn a completion certificate and assessment-based evaluation at program end.