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ercot

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End-to-end hourly ERCOT electricity demand forecasting: EIA + Open-Meteo data, leakage-safe features, walk-forward backtest (2.67% MAPE, 69% over baseline), CQR-calibrated 80% intervals, and a live interactive Streamlit Grid Planner.

  • Updated Jun 30, 2026
  • Python

Python optimization framework for municipal water utility demand response. Models a three-facility system (GWTP, WWTP, lift station) as a triple-battery against ERCOT day-ahead prices using MILP (Pyomo/GLPK) and NMPC (do-mpc/CasADi/Ipopt). ENG 573 Capstone, UIUC MEng Energy Systems, Spring 2026.

  • Updated May 1, 2026
  • Python

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