MetaCast - Natural Gas Demand Forecasting
Machine-learning forecasts for gas demand, load planning, and procurement decisions
AI, Forecasting, Machine Learning, Gas, Optimization
Overview
MetaCast forecasts gas consumption across hourly, day-ahead, seasonal, and planning horizons. It combines historical consumption, weather data, calendar effects, and optional economic drivers to produce forecasts with confidence intervals.
Key Features
- Multi-model Forecasting
- Weather & Calendar Features
- Confidence Intervals
- API Delivery & Retraining
Challenge
Gas utilities need reliable demand forecasts to plan procurement, storage, nominations, and network operations. Manual forecasting is slow to update, difficult to validate, and vulnerable to weather-driven seasonality and unusual consumption patterns.
Solution
MetaEnergy developed an ML forecasting engine using ensemble methods such as gradient-boosted trees, recurrent models, and statistical baselines. The platform automates data preparation, feature engineering, model comparison, retraining, and API delivery to operational systems.
Impact & Results
- Supports 10+ years of historical consumption data and forecasts for 100+ delivery points. In validated deployments, the approach can improve day-ahead and seasonal forecast quality while identifying measurable procurement and imbalance optimization opportunities.
Tech Stack
- Python
- TensorFlow
- Scikit-learn
- PostgreSQL
- FastAPI
- Docker