energy_trading_hub_builder

Scaffold a renewable energy trading hub with SCADA ingestion, SQL schema, SARIMA forecasting, and BI dashboard.

Updated Feb 26, 2026

Search Tags:#automation #postgresql #data-analytics #sarima #power-bi #scada #energy-trading

Ready to run

npx skills add https://github.com/DeathStroXX/Renewable-Energy-Trading-Hub --skill energy-trading-hub-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: energy_trading_hub_builder
Source: https://github.com/DeathStroXX/Renewable-Energy-Trading-Hub/tree/main/.agent
Command: npx skills add https://github.com/DeathStroXX/Renewable-Energy-Trading-Hub --skill energy-trading-hub-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, statsmodels, matplotlib, psycopg2-binary, jupyter, openpyxl, seaborn.

What problem does it solve?

Streamlines the setup and orchestration of a Renewable Energy Trading & Asset Forecasting hub, turning scattered project notes into an actionable, reusable workflow.

Core Features & Use Cases

End-to-end project scaffold: environment setup, data ingestion, synthetic data generation, SQL schema, forecasting, and BI visualization.
Workflow automation guidance: step-by-step scripts and file structures to accelerate delivery.
Use Case: a data analyst bootstraps data pipelines, writes database schemas, and generates a 7-day SARIMA forecast for turbine power.

Quick Start

Initialize the workspace, place T1.csv in data/raw, and run the trade generator to create energy_trades.csv.

Frequently Asked Questions about energy_trading_hub_builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build an end-to-end energy trading data pipeline from raw SCADA data?▼

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.

How does SARIMA forecasting work for renewable energy turbine power prediction?▼

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.

Do I need PostgreSQL to set up a renewable energy asset forecasting hub?▼

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.

What is the best way to automate synthetic energy trade generation for data analytics?▼

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.

Can I use Power BI for visualizing SCADA data and SARIMA forecasts from this workflow?▼

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.