About Workshop
Aim
What Participants Will Learn
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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
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
Important Dates
Registration Ends
Workshop Dates
What You Will Gain
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
Who Should Attend
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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