EnergyML_ForecastingPlatform

Universal energy forecasting platform that works with any electricity market worldwide

🎯 Project Overview

EnergyML_Forecasting is a market-agnostic energy forecasting platform designed to predict electricity ( load in first phase and prices in second phase ) across different regional markets. Built with production-grade architecture, the platform can adapt to any electricity market structure (US ISOs, European markets, Asian markets) with minimal configuration changes.

Current Focus: ERCOT (Texas) market as the first implementation Future Scope: CAISO, PJM, NYISO, European markets

🚀 Key Features

• Real-time data ingestion via GridStatus API
• Multi-model forecasting (Classical time series + Machine Learning)
• Market-agnostic architecture (easily extensible to new markets)
• REST API for programmatic access
• Live dashboard for visualization
• Production-ready deployment

📊 Technical Architecture

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────────┐
│ Data Source │ -> │ Data Pipeline│ -> │ ML Pipeline │ -> │ API/Frontend │
│ (GridStatus)│    │ (Normalize)  │    │ (Forecast)  │    │ (Serve)      │
└─────────────┘    └──────────────┘    └─────────────┘    └──────────────┘

Tech Stack

• Backend: Python, FastAPI
• ML/Data: pandas, scikit-learn, XGBoost
• Database: SQLite -> PostgreSQL
• API: GridStatus for energy data
• Deployment: Railway/Render
• Frontend: Streamlit/React

🎯 MVP Success Criteria

Technical Targets

• 24-hour ERCOT price predictions with <15% MAPE
• API response time <200ms
• Real-time data refresh every 5 minutes
• Multiple model comparison capabilities

Demo Objectives

• Live website showing current ERCOT forecasts
• Historical accuracy visualization
• Professional API documentation
• Scalable architecture demonstration

🏗️ Market-Agnostic Design

The platform is designed to work with any electricity market by:

1. Universal data schema - normalized format for any market
2. Configurable adapters - easy integration with new data sources
3. Generic feature engineering - works across market structures
4. Flexible model framework - adapts to different market patterns

Adding a new market requires:

• New data adapter configuration
• Market-specific feature mappings
• Validation rules adjustment
• No core architecture changes

🔧 Setup Instructions

Prerequisites

• Python 3.8+
• GridStatus API key

Installation

git clone https://github.com/yourusername/energyMlplatform.git
cd energyMlplatform
pip install -r requirements.txt

Configuration

# Create .env file
echo "GRIDSTATUS_API_KEY=your_api_key_here" > .env

Quick Start

from src.data import EnergyDataManager
from src.models import UniversalForecaster

# Initialize data manager for ERCOT
data_manager = EnergyDataManager(market="ERCOT")

# Get latest data
current_data = data_manager.get_latest_prices()

# Generate forecasts
forecaster = UniversalForecaster()
predictions = forecaster.predict_24h(current_data)

📚 Resources

• GridStatus API Documentation
• ERCOT Market Overview
• Energy Forecasting Papers

🤝 Contributing

This is a learning project showcasing energy market forecasting capabilities. Future enhancements:

• Additional market support (CAISO, PJM, European markets)
• Advanced ML models (LSTM, Transformer architectures)
• Real-time streaming capabilities
• Advanced risk management features

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Built by: Shankar Karki - Energy Quant
Contact: shankar.karki660@gmail.com