Program Structure

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.

Module 8: Energy Storage Analytics (Batteries and Hybrid Systems)

• Storage use cases: peak shaving, smoothing renewables, backup reliability.
• State of charge/health concepts: why they matter for performance.
• Charging/discharging strategies: rule-based vs optimization (overview).
• Lifecycle-aware operation: balancing performance and degradation.

Module 9: Grid Security, Trustworthy AI & Governance

• Cyber-physical risk basics: why grid AI needs strong safety controls.
• Data privacy (AMI) and secure handling concepts.
• Model risk: drift, adversarial signals, and reliability under rare events.
• Monitoring: alarms, audit trails, fallback modes, and human-in-the-loop design.

Module 10: Deployment Blueprint (From Model to Operations)

• Building an AI pipeline: ingestion → features → model → alert/forecast → dashboard.
• Integration with operations: SCADA/EMS/DMS touchpoints (conceptual).
• KPIs: outage duration reduction, forecast error reduction, loss reduction, peak reduction.
• Change management: operator trust, training, and continuous improvement.

Final Project

• Create an AI for Smart Grid Solution Blueprint for one use case.
• Include: problem statement, data sources, model approach, evaluation metrics, deployment workflow, and monitoring plan.
• Example projects: short-term load forecasting system, solar generation nowcasting workflow, transformer fault early warning, demand response targeting plan, storage dispatch optimization concept.