Gnarum | Empowering your energy
From forecast integration to intelligent model selection, Gnarum enhances reliability and performance across renewable portfolios.
As the energy transition accelerates, accurate generation forecasts for wind and solar portfolios are essential to reduce balancing costs and navigate the growing volatility of short-term electricity markets.
Gnarum delivers high-precision renewable generation forecasts that empower energy traders and Asset Managers to optimize operations. Built on the world’s most advanced Numerical Weather Prediction models and enriched with proprietary data sources, our in-house forecasting system interprets a carefully selected set of NWP models.
Our machine learning approach has consistently positioned us among the most reliable providers in the market for physical KPI performance, helping reduce balancing costs by up to 35%. Short-term market volatility and risk can significantly impact trading outcomes.
Gnarum’s probabilistic forecasting includes reliable confidence intervals that quantify uncertainty, helping energy professionals anticipate fluctuations and optimize their strategies accordingly.
With 24/7/365 service reliability and a scalable REST API, Gnarum ensures seamless integration across large wind and solar portfolios, supporting thousands of assets with ease.
The client had a long-standing collaboration with two forecast providers. Their forecast management process was limited to passive data storage, which was then forwarded to the risk management department. Trading strategies and arbitrage decisions were based on this stored data.
Our goal was to transform the client's role from a passive data repository into an active data processor. The objective was to monitor and enhance the quality of physical forecast data before it reached the risk management department.
In addition to delivering our own forecasts, we implemented a multi-provider setup on the client side. This enabled us to generate an additional forecast using ensemble learning techniques, which combine multiple available forecasts into a single, more accurate and robust prediction.
Furthermore, by defining a sliding window tailored to each asset, and implementing a dynamic, automatic switching mechanism, we ensured that the most accurate forecast—based on a shared frequency and aligned with an agreed KPI—was consistently delivered. This approach led to measurable improvements in forecast reliability and supported more informed decision-making.
The client had a long-standing collaboration with two forecast providers. Their forecast management process was limited to passive data storage, which was then forwarded to the risk management department. Trading strategies and arbitrage decisions were based on this stored data.
Our goal was to transform the client's role from a passive data repository into an active data processor. The objective was to monitor and enhance the quality of physical forecast data before it reached the risk management department.
Our goal was to transform the client's role from a passive data repository into an active data processor. The objective was to monitor and enhance the quality of physical forecast data before it reached the risk management department.
In addition to delivering our own forecasts, we implemented a multi-provider setup on the client side. This enabled us to generate an additional forecast using ensemble learning techniques, which combine multiple available forecasts into a single, more accurate and robust prediction. Furthermore, by defining a sliding window tailored to each asset, and implementing a dynamic, automatic switching mechanism, we ensured that the most accurate forecast—based on a shared frequency and aligned with an agreed KPI—was consistently delivered. This approach led to measurable improvements in forecast reliability and supported more informed decision-making.
