Artificial Intelligence for Smart Energy Grids

Module 1: Smart Energy Grids — What Makes Them “Smart”

• Traditional grid vs smart grid: sensing, automation, and real-time decision making.
• Key components: generation, transmission, distribution, consumers, prosumers.
• Grid challenges: peak demand, outages, losses, variability, and aging assets.
• Where AI fits: prediction, detection, optimization, and decision support.

Module 2: Grid Data Ecosystem (SCADA, AMI, IoT, Weather)

• SCADA and substation data: what it looks like and how it is used.
• Smart meters (AMI): interval data, consumption patterns, and privacy basics.
• Power quality data: voltage, frequency, harmonics (overview).
• Data issues: missing values, sensor drift, latency, and noisy measurements.

Module 3: Load Forecasting (Short-Term to Long-Term)

• Why forecasting matters: dispatch, purchase planning, and reliability.
• Features: weather, calendar effects, events, and demand history.
• Models overview: regression, tree-based models, time-series ML, deep learning (conceptual).
• Evaluation: MAE/RMSE, peak error, and operationally meaningful metrics.

Module 4: Renewable Forecasting & Integration (Solar/Wind Variability)

• Solar/wind variability: ramp events and uncertainty.
• Weather-driven forecasting: irradiance, cloud cover, wind speed inputs.
• Nowcasting vs day-ahead planning: where each is used.
• Grid integration strategies: curtailment, storage, and flexible demand (overview).

Module 5: Anomaly Detection & Fault Prediction

• Outage detection: abnormal patterns in meter/SCADA data.
• Transformer and feeder health monitoring: early-warning signals.
• Unsupervised methods overview: clustering, isolation methods, autoencoders (concept).
• Predictive maintenance workflow: alerts, triage, and field action loops.

Module 6: Demand Response & Consumer-side Intelligence

• Demand response basics: shifting load vs shedding load.
• Customer segmentation: identifying flexible loads and high-impact users.
• Dynamic pricing and behavior response (overview).
• Measuring impact: baseline modeling and verification concepts.

Module 7: Optimization & Control for Grid Operations

• Operational goals: reliability, cost, losses, voltage stability.
• Optimization overview: unit commitment, economic dispatch (conceptual).
• Voltage/VAR control and distribution automation (overview).
• AI for decision support: recommendations with constraints and operator override.