energy_trading_hub_builder

To build an end-to-end energy trading pipeline, ingest raw SCADA data, generate synthetic trades, create a SQL schema, run SARIMA forecasting, and prepare a BI dashboard. This workflow automates environment setup and ensures reproducible file structures across data visualization steps.

SARIMA forecasting for turbine power uses historical SCADA data to model seasonal patterns and generate 7-day predictions. It leverages statsmodels within a structured pipeline to transform raw time-series data into actionable forecasting outputs for energy trading.

PostgreSQL is used to build the SQL schema for the renewable energy forecasting hub. The workflow requires psycopg2-binary to connect the database, storing ingested SCADA data and synthetic trades for downstream SARIMA modeling and BI visualization.

The best way to automate synthetic energy trade generation is to place raw T1.csv data into a data directory and run a trade generator script. This creates an energy_trades.csv file, providing standardized datasets for SARIMA modeling and BI dashboard visualization.

Power BI is used to prepare the BI dashboard for visualizing SCADA data and SARIMA forecasts. The workflow outputs structured data from the SQL schema and modeling steps, enabling integration with Power BI for renewable energy trading and asset forecasting visualization.