Transformer-LSTM Hybrid Forecast Engine for RE + Storage Dispatch
Forecast Smarter, Dispatch Better: Hybrid AI for Renewable + Storage
About This Course
The Transformer–LSTM Hybrid Forecast Engine for RE + Storage Dispatch workshop is a three-day, hands-on sprint from data prep to operations. You’ll build time-aligned SCADA+weather datasets, train a hybrid Transformer (weather) + LSTM (plant history) model with calibrated multi-horizon outputs (P10/P50/P90), and evaluate via rolling backtests. Finally, you’ll drive battery dispatch using rolling-horizon MPC, producing dispatch/SoC trajectories and a KPI dashboard (cost savings, reserve compliance, curtailment avoided, VFF).
Aim
Enable participants to build and deploy a Transformer–LSTM hybrid that delivers calibrated multi-horizon RE forecasts (P10/P50/P90) and converts them into optimal battery dispatch via rolling-horizon MPC, with measurable KPI gains.
Workshop Objectives
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Build time-aligned SCADA+weather datasets with time-aware splits
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Engineer features; establish persistence/LSTM baselines
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Train a late-fusion Transformer–LSTM with P10/P50/P90 outputs
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Ensure training hygiene and uncertainty calibration
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Evaluate via rolling backtests; create a compact model card
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Convert forecasts to MPC-based battery dispatch under constraints
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Operationalize (serving, drift, shadow runs, HMI) and track KPIs
Workshop Structure
📅 Day 1 – Data & Hybrid Basics
- Signals & horizons: solar/wind, net load, price; intraday/day-ahead use cases
- Time-aware splits; feature engineering (lags/rolls, weather look-ahead, plant metadata, calendar)
- Metrics: RMSE/sMAPE, multi-horizon, pinball loss; baseline design (persistence)
- Hybrid idea: Transformer (exogenous weather) + LSTM (plant history) with late fusion
- Hands-on: Build a time-aligned SCADA+weather table and train persistence + single-layer LSTM baselines
📅 Day 2 – Train the Transformer–LSTM & Calibrate
- Architecture: seq lengths, encoder–decoder attention, LSTM history stream, fusion, multi-task heads
- Training hygiene: scaling, scheduled sampling, dropout/WD, gap handling, early stop
- Uncertainty: quantile heads, ensembles, conformal calibration; P10/P50/P90 outputs
- Evaluation: rolling-origin backtests; error by regime/hour
- Hands-on: Train the hybrid model for multi-horizon forecasts; emit P10/P50/P90 and a compact backtest vs baselines
📅 Day 3 – Forecast→Storage Dispatch (MPC) & Ops
- Battery model: SoC bounds, power limits, efficiency, degradation proxy, reserves
- Optimization: rolling-horizon MPC using forecast ensembles; objectives—arbitrage, ramp smoothing, peak shaving
- Operations: DA/RT alignment, penalties, fail-safes for bad forecasts; MLOps (serving, drift, shadow runs, HMI)
- KPIs: cost savings, reserve compliance, curtailment avoided, VFF (value of forecast)
- Hands-on: Implement MPC dispatch consuming P50/P10/P90; output dispatch/SoC trajectories and KPI dashboard
Who Should Enrol?
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Data scientists, ML engineers, and MLOps practitioners in energy/utilities
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Power system/renewables engineers, grid operators, and energy analysts
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Battery/storage planners, asset managers, and virtual power plant (VPP) teams
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Professionals at utilities, IPPs, RE developers, aggregators, and microgrid operators
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Quant/optimization folks working on forecasting→dispatch workflows (DA/RT)
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Prereqs (helpful, not mandatory): Python + time-series ML; basics of PyTorch/TF; familiarity with SCADA/weather data; introductory optimization/MPC concepts
Important Dates
Registration Ends
11/18/2025
IST 4:30 PM
Workshop Dates
11/18/2025 – 11/20/2025
IST 5:30 PM
Workshop Outcomes
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Clean, time-aligned SCADA+weather dataset with strong features
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Calibrated Transformer–LSTM multi-horizon forecasts (P10/P50/P90)
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Robust evaluation: rolling backtests, regime/hour diagnostics, model card
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Forecast uncertainty managed (quantiles, ensembles, conformal)
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Battery dispatch via rolling-horizon MPC with SoC/limits/efficiency
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End-to-end pipeline to KPIs: cost savings, reserve compliance, curtailment avoided, VFF
Fee Structure
Student
₹1999 | $65
Ph.D. Scholar / Researcher
₹2999 | $75
Academician / Faculty
₹3999 | $85
Industry Professional
₹5999 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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