Mid-Term Demand Forecasting
Using ECMWF Weather Models & AI/ML Modeling for Energy Markets

As extreme weather patterns accelerate and energy markets grow more volatile, forward-looking organizations are reevaluating how they model demand at the sub-seasonal and seasonal horizon.

This white paper outlines Amperon's new forecasting framework that combines the robustness of ECMWF’s sub-seasonal ensemble data with proprietary machine learning interpolation techniques.

The result is a significant advancement in accuracy and granularity for mid-term energy demand forecasts.

In this white paper, we examine:

• ‍An in-depth look at known biases in traditional seasonal weather models and how to correct for them
• The importance of dynamically integrating climate signals such as ENSO and NAO
• A technical breakdown of Amperon’s hybrid forecasting approach and why it outperforms static methods
• Strategic applications for traders, grid operators, and utilities managing long-range risk and hedging positions