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Professional Certification Program
The TransformerāLSTM Hybrid Forecast Engine for RE + Storage Dispatch workshop is a three-day, hands-on sprint from data prep to operations. Youāll 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, youāll drive battery dispatch using rolling-horizon MPC, producing dispatch/SoC trajectories and a KPI dashboard (cost savings, reserve compliance, curtailment avoided, VFF).
Enable participants to build and deploy a TransformerāLSTM 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.
Build time-aligned SCADA+weather datasets with time-aware splits
Engineer features; establish persistence/LSTM baselines
Train a late-fusion TransformerāLSTM with P10/P50/P90 outputs
Ensure training hygiene and uncertainty calibration
Evaluate via rolling backtests; create a compact model card
Convert forecasts to MPC-based battery dispatch under constraints
Operationalize (serving, drift, shadow runs, HMI) and track KPIs
Data scientists, ML engineers, and MLOps practitioners in energy/utilities
Power system/renewables engineers, grid operators, and energy analysts
Battery/storage planners, asset managers, and virtual power plant (VPP) teams
Professionals at utilities, IPPs, RE developers, aggregators, and microgrid operators
Quant/optimization folks working on forecastingādispatch workflows (DA/RT)
Prereqs (helpful, not mandatory): Python + time-series ML; basics of PyTorch/TF; familiarity with SCADA/weather data; introductory optimization/MPC concepts
11/18/2025
IST 4:30 PM
11/18/2025 ā 11/20/2025
IST 5:30 PM
Clean, time-aligned SCADA+weather dataset with strong features
Calibrated TransformerāLSTM multi-horizon forecasts (P10/P50/P90)
Robust evaluation: rolling backtests, regime/hour diagnostics, model card
Forecast uncertainty managed (quantiles, ensembles, conformal)
Battery dispatch via rolling-horizon MPC with SoC/limits/efficiency
End-to-end pipeline to KPIs: cost savings, reserve compliance, curtailment avoided, VFF
ā¹1999 | $65
ā¹2999 | $75
ā¹3999 | $85
ā¹5999 | $105
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PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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