Electricity Forecasting Framework

This package appears coherent for electricity forecasting. Before running it: (1) review the full scripts (especially deploy_model.py, batch_forecast/send_to_downstream and any truncated functions) to confirm any network endpoints they call; (2) supply API keys only for trusted weather/data providers and store them securely (env vars or secret manager), since example code shows placeholders; (3) run in an isolated virtual environment or container and avoid running as root (batch job writes to /var/log and reads/writes models/ and forecasts/ directories); (4) pin dependencies and audit third‑party libraries (PyTorch, LightGBM, joblib, FastAPI, requests, etc.) before deploying to production. If you want, I can scan the specific scripts (deploy_model.py, batch_forecast, send_to_downstream) for any network calls or unexpected behavior.