Transformer-LSTM Hybrid Forecast Engine for RE + Storage Dispatch

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

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

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