Transformer-LSTM Hybrid Forecast Engine for RE + Storage Dispatch

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.

• Build time-aligned SCADA+weather datasets with time-aware splits

• Engineer features; establish persistence/LSTM baselines

• Train a late-fusion Transformer–LSTM 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 forecasting→dispatch workflows (DA/RT)

• Prereqs (helpful, not mandatory): Python + time-series ML; basics of PyTorch/TF; familiarity with SCADA/weather data; introductory optimization/MPC concepts

• Clean, time-aligned SCADA+weather dataset with strong features

• Calibrated Transformer–LSTM 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