AI for Renewable Energy Forecasting (Solar, Wind, Hydro)

Overview

Build ML forecasting pipelines that improve renewable integration. You’ll combine numerical weather prediction (NWP), satellite/radar imagery, river inflow & reservoir data, and plant SCADA to produce reliable forecasts for solar PV, wind farms, and hydro assets.

Curriculum

• Data sources: NWP models, satellite irradiance, radar winds/rain, SCADA & telemetry.
• Feature engineering: clearsky index, cloud motion vectors, shear/roughness, snowpack & inflows.
• Model patterns: GBMs, random forests, temporal CNN/RNN, TFT; hybrid physics-ML.
• Probabilistic forecasting: quantile regression, ensembles, calibration & sharpness.
• Horizons: nowcasting (0–6h), intraday (6–24h), day-ahead (24–72h).
• Evaluation: MAPE/MAE/CRPS, skill vs persistence, backtesting & leakage control.
• Operations: storage & hydro scheduling, ramping, curtailment, bidding strategies.
• Dashboards & MLOps: retraining cadence, drift monitoring, explainability & alerts.
• Case study: portfolio-level forecast aggregation and uncertainty-aware dispatch.

Benefits of Attending

• Faster, more accurate forecasts that reduce imbalance and curtailment.
• Ready-to-use templates for data ingestion, feature sets, and evaluation.
• Clear playbooks to operationalize forecasts with traders and grid teams.
• Confidence to communicate uncertainty to business stakeholders.

What You Will Learn

By the end of this program, you will be able to:

• Assemble data pipelines blending weather, imagery, and SCADA for renewables.
• Train deterministic & probabilistic models and interpret forecast skill.
• Deploy horizon-specific forecasts for solar, wind, and hydro operations.
• Integrate outputs into scheduling, storage dispatch, and market bids.

Practitioner Profile