EnergiCast

EnergiCast is a domain‑specific Python library for energy forecasting (load, PV, wind, prices) that combines:

• ⚡ Domain‑aware AutoML with hierarchical reconciliation and energy‑weighted cost metrics,
• 🌞 Physics–ML hybrids (pvlib solar/wind features + ML/DL models),
• 🔄 Synthetic gap filling with seasonal and generative imputation constrained by physics,
• 🌦️ Weather scenarios with ensemble perturbations for uncertainty quantification,
• 📊 Benchmarks (OpenSTEF‑like datasets) and standardized backtesting protocols.

✨ Features

• AutoML: tailored search spaces for energy time series (ARIMA, ETS, XGB, TFT roadmap).
• Physics priors: PV/wind features from pvlib and meteorological signals.
• Synthetic imputation: diffusion/GAN roadmap, with ramp‑rate & non‑negativity constraints.
• Hierarchical reconciliation: MinT‑style coherent forecasts (asset → feeder → system).
• Metrics: pinball loss, CRPS, and energy‑weighted MAPE with price‑aware penalties.
• Deployment: simple model export and CLI tools.

🚀 Quickstart

Install in editable mode for development:

git clone https://github.com/TyMill/energicast.git
cd energicast
pip install -e .

Run the end-to-end workflow on the bundled example data:

python -m energicast.cli train --config examples/pv_config.yaml --out runs/demo_model
python -m energicast.cli backtest --config examples/pv_config.yaml --out runs/demo_backtest
python -m energicast.cli export --model-dir runs/demo_model --fmt pickle
python -m energicast.cli report --backtest-dir runs/demo_backtest

📚 Dokumentacja

Dokumentację można zbudować lokalnie przy pomocy MkDocs:

pip install -e .[docs]
mkdocs serve

Po wdrożeniu GitHub Pages będzie dostępne pod adresem https://TyMill.github.io/energicast/.

📂 Repository Structure

energicast/
├─ pyproject.toml
├─ src/energicast/
│  ├─ data/        # loaders for entsoe, pvlib, weather
│  ├─ impute/      # synthetic gap filling
│  ├─ features/    # calendar + energy features
│  ├─ models/      # ARIMA/ETS, XGB, TFT (roadmap)
│  ├─ hier/        # reconciliation
│  ├─ automl/      # Optuna‑based AutoML
│  ├─ metrics/     # probabilistic + energy metrics
│  ├─ scenarios/   # weather ensembles
│  ├─ bench/       # benchmark datasets
│  ├─ deploy/      # export utils
│  └─ cli.py       # Typer‑based CLI
└─ examples/       # demo scripts & notebooks

👨‍💻 Authors

Created by

• Dr Tymoteusz Miller, University of Szczecin,
• Dr inz. Ewelina Kostecka, Maritime University of Szczecin.

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.