With cpX.AI and cpX.Energy, EnBW implements AI-powered gas demand forecasting and optimises the cost-effective management of its balancing groups.
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EnBW Energie Baden-Württemberg AG (EnBW) utilises Sopra Steria’s cpX.Energy SaaS platform for its balancing group management. The solution supports the processing and provision of data relevant to balancing and establishes a consistent foundation for the implementation of requirements of the German gas market’s regulatory framework, especially in the context of balancing regulations according to GaBi Gas.
Accurate sales and demand forecasts are integral factors of success for energy traders. However, volatile market conditions, changing consumption patterns, and regulatory frameworks are constantly increasing the complexity of balancing group management. The more accurately forecasts reflect actual energy demand, the lower the balancing deviations and the resulting balancing group and procurement costs will be.
cpX.AI was added as an AI-based forecasting service to the platform. Forecast values are generated automatically and directly integrated into existing balancing group processes.
The facts
- AI-based gas demand forecasts: With cpX.AI, EnBW has added an AI-powered forecasting service to the cpX.Energy platform that automatically generates sales and demand forecasts.
- Seamless integration into balancing group management: The forecast values are calculated several times a day and automatically incorporated into existing balancing group processes on cpX.Energy.
- Improved forecast quality: As part of a proof-of-concept project lasting several months, cpX.AI was able to demonstrate higher forecasting accuracy than previously used methods.
- Increased efficiency and reduced costs: The solution supports more cost-effective balancing group management through a higher degree of automation and reduced balancing deviations, helping to lower balancing energy and procurement costs.
The challenge
For energy companies like EnBW, accurate forecasts are a key factor for success. Even slight deviations between forecasted and actual energy volumes can lead to significant balancing energy costs and increased balancing group management expenses. Additionally, changing market conditions, volatile consumption patterns, and regulatory requirements are increasing the demand on speed, transparency and forecast accuracy.
EnBW therefore sought a solution that would not only replace existing forecasting methods but also improve their quality and fully integrate them into operational processes. A particular focus was placed on the traceability of the forecasting models, the rapid processing of large volumes of data, and automated integration into the existing balancing group management on cpX.Energy.
The result
As part of a proof-of-concept project lasting several months, cpX.AI demonstrated that its generated sales forecasts were of higher quality than those produced by previously used methods. Next to forecast accuracy, the primary factor for success was end-to-end automation: Actual data is imported from cpX.Energy and supplemented with current influencing factors, recalculated several times a day and then automatically fed back to the business module for balancing group management.
Historical measured values, together with external factors such as weather data and (holiday) calendars, form the basis for the cyclical training of the forecasting models. An integrated monitoring system helps EnBW assess completeness, anomalies, forecast accuracy and forecast errors.
The solution is able to generate forecasts for several hundred metering points within just a few minutes. It also supports cold start scenarios for new metering points on the basis of historical load profiles and additional influencing factors provided to the AI model. Following a successful period of parallel operation, EnBW has adopted cpX.AI as its primary forecasting system.
The result is a higher degree of automation, demonstrably improved forecast accuracy and more cost-effective balancing group management by reducing balancing deviations and their associated costs.
For Tanja Neff, cpX.Energy product owner for EnBW-Handel, the Sopra Steria platform combines powerful AI capabilities with a practical and user-friendly interface, enabling EnBW to make more accurate forecasts.
Matthias Reick, IT solution manager (gas) for EnBW-Handel, was particularily impressed by the seamless and rapid integration of cpX.AI into the long-established .BCM system on cpX.Energy.
About the customer
EnBW Energie Baden-Württemberg AG (EnBW) is one of the largest energy companies in Germany and Europe. The company is active across the entire value chain of the energy industry – from generation, trading and transport to distribution and innovative energy and infrastructure projects.
As part of its digitalisation strategy, EnBW focuses on modern platforms, cloud technologies, and data-driven processes. In collaboration with Sopra Steria, this resulted in a solution that combines modern AI methods with the expertise of the energy industry, laying the foundation for data-driven and cost-effective balancing group management.