NZAI-AI-206
AI for Energy Trading & Price Forecasting
Build practical AI pipelines for power & gas price forecasting and trading strategy support. Blend market data, fundamentals, weather/NWP, outages, and flows to produce horizon-specific forecasts, scenario bands, and operator-ready trade signals with sound backtesting and risk controls.
Secure your spot now — limited seats remaining!
Horizon Coverage
Intra-day, day-ahead, week-ahead & seasonal views.
Data Fusion
Market prices, fundamentals, outages, flows & weather/NWP.
Probabilistic Bands
Quantiles/ensembles, scenario curves & risk overlays.
Trading Integration
Backtests, PnL attribution, hedging & bid support.
Overview
Translate market & fundamental signals into robust power/gas price forecasts and trade ideas. You’ll learn feature engineering for weather, demand, outages and interconnectors, build deterministic and probabilistic models, and evaluate them with leakage-safe backtests and trading-aware metrics.
Curriculum
- Data landscape: day-ahead & real-time prices, curves, bids/offers, fundamentals, outages, flows, weather/NWP.
- Feature engineering: load/renewables, degree days, anomalies, calendar/holiday, supply stacks & fuel spreads.
- Models: gradient boosting/GBM, random forests, temporal CNN/RNN/TFT; hybrid physics-ML approaches.
- Probabilistic forecasting: quantile/ensemble methods, CRPS calibration, scenario generation & stress tests.
- Backtesting: rolling/walk-forward, leakage control, stability checks, regime shifts & model ensembling.
- Metrics: MAE/MAPE/RMSE, pinball/CRPS, directional accuracy & trading utility measures.
- Strategy linkage: spread/arbitrage, storage/virtual storage, hedging playbooks, bid support.
- Risk & governance: PnL attribution, limits, VaR-style thinking, model monitoring & explainability.
- Dashboards: forecast bands, error attribution, confidence overlays & trader workflows.
- Case study: day-ahead power forecast to hedge/bid decisions with PnL review.
What Will You Learn
By the end of this program, you will be able to:
- Assemble data pipelines that blend market, fundamentals and weather for price forecasts.
- Train deterministic & probabilistic models and interpret skill vs. trading value.
- Run leakage-safe backtests and convert forecasts into hedging/bidding actions.
- Publish trader-ready dashboards with scenario bands and risk/limit context.
Practitioner Profile
Delivered by practitioners who have built trading analytics and forecasting systems for utilities, IPPs, traders and grid operators across multiple markets.
Emphasis on practical, explainable models and decision support—not black-box complexity.