This Energy Demand Forecast module, powered by Gnarum, helps energy retailers anticipate demand with confidence, combining advanced analytics and intelligent models to deliver reliable consumption forecasts and support informed market decisions.
Demand Scope
Advanced demand forecasting for energy suppliers
Energy Supply Retail
By combining algorithms and machine learning with meteorological and consumption data, the Forecast module enables energy retailers to accurately anticipate supply requirements, align procurement strategies with demand, mitigate imbalance risk, and optimize portfolio performance.
Energy Supply Industrial
By applying an advanced set of temporal and meteorological features, combined with classical statistical techniques and state-of-the-art machine learning algorithms, we are helping energy retailers reduce imbalance at delivery points where traditional forecasting tools—typically embedded in ERP systems—reach their limits, thereby enhancing their operations across all market horizons.
Inside Demand Forecast
A proven solution trusted by leading energy suppliers. Our AI-driven demand forecasting system delivers high levels of accuracy in predicting consumption patterns, even across complex and highly diversified portfolios, enabling more efficient and reliable energy operations.
Monitor demand forecasts, analyze consumption behavior, and track deviations through a powerful web platform—while seamlessly integrating forecast data into your systems via a flexible, easy-to-use API.
High-accuracy consumption forecasting
Significant QH imbalance reduction
15-minute market granularity
Advanced Capabilities
Built to handle complex demand portfolios with accuracy and flexibility
Advanced time series modeling
ML / AI with continuous and adaptive training
Incorporates leading predictive models for weather-related variables
Dynamic clustering tailored to the specific structure of the consumption points portfolio, with individualized management of high-consumption points (6.x)
Includes probabilistic calibration using
Conformal Prediction techniques for risk management in large, hard-to-forecast portfolios
Flexible and customizable
Modeling aligned with the structural characteristics of each portfolio
Configurable grouping of CUPS based on monitoring and operational needs
Forecast updates for Day-Ahead and Intraday Adjustment markets (IDAs)
Integration with external systems (REE, ERPs, etc.)
Dynamic integration and high-frequency updates of portfolios and measurements
Aggregation to plant-level buses, facilitating integration with trading systems
Gnarum’s forecasting demand module helps retailers navigate Spain’s new quarter-hourly market by combining AI/ML, ensemble learning, and high-resolution data to deliver accurate, real-time predictions for large, complex portfolios—reducing imbalances and optimizing market operations.