Cohort: 1 

Location: Beijing, China 

Application Title: AI forecasting models for energy trading and market optimization 

Team Members: Chi Wang, Jiabin Lu, Tom Lu, Ray Pang, Wenyuan Bao

The challenge 

Electricity markets are highly sensitive to weather. Wind speed, cloud cover and temperature directly affect how much renewable energy is produced and how demand fluctuates. For energy traders, even small forecast errors can translate into financial risk, especially in short-term electricity spot markets. 

Traditionally, weather forecasts used in energy trading rely on physics-based numerical models. These models simulate atmospheric behaviour using complex equations and large computing resources. While accurate, they are slow to run and difficult to update frequently. This limits how often forecasts can be refreshed and how finely they can be resolved at the local level. 

As renewable generation grows, markets become more volatile. Traders need forecasts that are not only accurate but also fast enough to support daily decision-making across large portfolios. Conventional models struggle to meet this requirement at scale. 

The solution 

A transformer-based weather forecasting system for energy markets 

Horizon Power partnered with TerraQuanta to deploy an AI-driven weather forecasting system designed specifically for electricity market use. 

The system replaces traditional physics-based forecasting with a transformer-based model trained on more than four decades of historical weather data. For non-specialist readers, transformer models are a class of AI systems that learn patterns from large datasets and can generate predictions quickly once trained. In this case, the model learns how atmospheric conditions evolve over time without having to simulate every physical interaction explicitly. 

The forecasting system integrates global meteorological records with real-time observations, allowing it to produce short- and medium-range forecasts suitable for trading decisions. 

How it works 

From weather prediction to market insight 

The model ingests satellite observations and terrain data to generate high-resolution forecasts of wind conditions and solar radiation. These forecasts are updated several times a day and can resolve local effects that are important for renewable generation. 

Because the model runs much faster than conventional numerical simulations, forecasts can be produced with significantly lower computational cost. This allows traders to explore scenarios and update strategies more frequently. 

Forecast outputs are then linked to electricity price models. By understanding how weather patterns influence supply and demand, the system supports more informed trading decisions across Horizon Power’s portfolio. 

How it differs from conventional approaches 

Speed and resolution over heavy simulation 

Conventional weather forecasting relies on repeated physical simulations that consume large amounts of computing time. As a result, forecasts are expensive to generate and difficult to scale across different regions or time horizons. 

The TerraQuanta–Horizon Power system relies on learned representations of atmospheric behaviour rather than continuous physical simulation. This reduces computational overhead and enables faster forecasting at finer spatial resolution. The result is a practical forecasting tool that aligns better with the pace of electricity markets. 

Impact 

• Forecasting efficiency improved by a factor of 50,000 compared with traditional methods 
• Deployed across a 2,000 GWh annual electricity trading portfolio 

Why it matters 

Supporting decision-making in weather-driven markets 

As electricity systems incorporate more renewable energy, weather becomes a central driver of market behaviour. The collaboration between Horizon Power and TerraQuanta shows how AI-based forecasting can help traders respond more quickly to changing conditions. 

By combining long-term meteorological data with fast inference, the system supports more timely decisions without increasing computational burden. This approach becomes increasingly relevant as energy markets continue to evolve.