📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Transformer LSTM Hybrid Forecast Engine for RE Storage Dispatch, from foundations to a certified capstone project.
Outline
Understand signals and horizons for solar/wind, net load, and price in intraday/day-ahead scenarios. • Apply time-aware data splits and engineer features (lags/rolls, weather look-ahead, plant metadata, calendar). • Define metrics like RMSE/sMAPE and multi-horizon pinball loss; design persistence baselines. • Grasp the hybrid concept: Transformer for exogenous weather and LSTM for plant history with late fusion.
Outline
Explore Transformer-LSTM architecture details including sequence lengths, encoder–decoder attention, LSTM history streams, fusion, and multi-task heads. • Implement training hygiene practices: scaling, scheduled sampling, dropout/weight decay, gap handling, and early stopping. • Integrate uncertainty quantification: quantile heads, ensembles, and conformal calibration for P10/P50/P90 outputs. • Perform rolling-origin backtests for evaluation; analyze error by regime and hour.
Outline
Construct comprehensive battery models including SoC bounds, power limits, efficiency, degradation proxies, and reserves. • Implement optimization using rolling-horizon Model Predictive Control (MPC) with forecast ensembles. • Define optimization objectives: arbitrage, ramp smoothing, and peak shaving. • Understand operational considerations: DA/RT alignment, penalties, and fail-safes for inaccurate forecasts.
Outline
Track key performance indicators (KPIs) such as cost savings, reserve compliance, curtailment avoided, and VFF (Value of Forecast). • Build time-aligned SCADA+weather datasets with time-aware splits. • Engineer features; establish persistence/LSTM baselines. • Ensure training hygiene and uncertainty calibration.
Earn government-registered certification in Transformer LSTM Hybrid Forecast Engine for RE Storage Dispatch
e-Certificate and e-Marksheet issued on successful completion.
