Data Science

Forecasting and optimization for district heating dispatch — EnergyTrading2026 project seminar.

Teams

• Forecasting — demand forecasts (hourly, multi-horizon)
• Optimization — hourly MPC dispatch on top of the forecast

Setup

Requires Python ≥ 3.11.

git clone git@github.com:EnergyTrading2026/datascience.git
cd datascience
pip install -e .

This installs the optimization stack (numpy, pandas, pyarrow, matplotlib, pyomo, highspy, apscheduler). Notebooks need pip install -e '.[notebooks]' (adds jupyter, ipykernel). Forecasting notebooks pull in additional packages (scikit-learn, statsmodels, tensorflow, pmdarima, xgboost) that you'll need to install separately.

This editable install is the intended setup for local development and tests. The intended production path for the optimization service is Docker — see docs/deploy/README.md.

Project Structure

src/            Production code (forecasting + optimization)
notebooks/      Jupyter notebooks for exploration and prototyping
tests/          Automated tests (src/ only — notebooks aren't tested)
docs/           Problem definitions and methodology notes
data/           Data (raw and cleaned)

Optimization context lives in docs/optimization/: problem statement (optimization_problem.md) and hourly MPC notes (hourly_mpc.md).

Entry points

• optimization-backtest — runs the hourly MPC backtest harness (defined in pyproject.toml, see src/optimization/backtest.py).
• Dockerized optimization service — see docs/deploy/README.md for the production daemon, first-time setup, synthetic forecast smoke test, and operational commands.
• scripts/sim_forecaster.py — local smoke-test helper that writes a contract-shaped forecast parquet into data/forecast/ so the Docker daemon can run a full optimization cycle without the real forecasting service.

Workflow

1. Create a feature branch from main
2. Make your changes
3. Open a pull request
4. Get 1 approval
5. Merge