AI Gas Demand Forecasting

Intelligent forecasting of natural gas demand patterns

The Problem

“Accurate gas demand forecasts amid volatile drivers”

Organizations face these key challenges:

Peak-day and intraday forecast errors drive high imbalance charges, emergency purchases, and operational stress on storage and pipeline capacity.

Traditional HDD-based models miss nonlinear effects (wind chill, humidity), distributed generation impacts, and sudden behavioral/industrial changes, especially during extreme weather.

Data fragmentation across SCADA, AMI, nominations, weather vendors, and market prices slows updates and limits transparency into forecast confidence and drivers.

Impact When Solved

10–25% better day-ahead accuracy and 15–30% lower imbalance exposure through probabilistic, continuously updated forecasts.0.5–2.0% reduction in seasonal supply and capacity costs via improved procurement timing and storage injection/withdrawal optimization.30–50% less manual forecasting effort with automated data ingestion, anomaly detection, and explainable driver attribution for operators and traders.

Real-World Use Cases

Short-term Multi-Regional Load Forecasting with Dynamic Spatial Features and Missing Data

This is like a smart weather forecast, but for electricity demand across many regions at once. It learns how power usage in one area affects nearby areas over time, keeps updating those relationships as they change, and still makes good predictions even when some of the input data is missing or noisy.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0